How to cite this paper
Ghorbani, R & Ghousi, R. (2019). Predictive data mining approaches in medical diagnosis: A review of some diseases prediction.International Journal of Data and Network Science, 3(2), 47-70.
Refrences
Al-Maqaleh, B. M., & Abdullah, A. M. G. (2017). Intelligent predictive system using classification tech-niques for heart disease diagnosis. International Journal of Computer Science Engineering (IJCSE), 6(6), 145-151.
Alizadehsani, R., Habibi, J., Bahadorian, B., Mashayekhi, H., Ghandeharioun, A., Boghrati, R., & Sani, Z. A. (2012). Diagnosis of coronary arteries stenosis using data mining. Journal of medical signals and sensors, 2(3), 153-159.
Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., ... & Sani, Z. A. (2013). A data mining approach for diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 111(1), 52-61.
Anunciaçao, O., Gomes, B. C., Vinga, S., Gaspar, J., Oliveira, A. L., & Rueff, J. (2010). A data mining approach for the detection of high-risk breast cancer groups. In Advances in Bioinformatics (pp. 43-51). Springer, Berlin, Heidelberg.
Babu, S., Vivek, E. M., Famina, K. P., Fida, K., Aswathi, P., Shanid, M., & Hena, M. (2017, April). Heart disease diagnosis using data mining technique. In Electronics, Communication and Aerospace Tech-nology (ICECA), 2017 International conference of (Vol. 1, pp. 750-753). IEEE.
Baihaqi, W. M., Setiawan, N. A., & Ardiyanto, I. (2016, August). Rule extraction for fuzzy expert system to diagnose coronary artery disease. In Information Technology, Information Systems and Electrical Engineering (ICITISEE), International Conference on (pp. 136-141). IEEE.
Bellaachia, A., & Guven, E. (2006). Predicting breast cancer survivability using data mining tech-niques. Age, 58(13), 10-110.
Bhargava, N., Dayma, S., Kumar, A., & Singh, P. (2017, January). An approach for classification using simple CART algorithm in WEKA. In Intelligent Systems and Control (ISCO), 2017 11th International Conference on (pp. 212-216). IEEE.
Bhatla, N., & Jyoti, K. (2012). An analysis of heart disease prediction using different data mining tech-niques. International Journal of Engineering, 1(8), 1-4.
Burke, H. B., Goodman, P. H., Rosen, D. B., Henson, D. E., Weinstein, J. N., Harrell Jr, F. E., ... & Bostwick, D. G. (1997). Artificial neural networks improve the accuracy of cancer survival predic-tion. Cancer, 79(4), 857-862.
Chang, W. P., & Liou, D. M. (2008). Comparison of three data mining techniques with genetic algorithm in the analysis of breast cancer data. J Telemed Telecare, 9(1), 26.
Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology, 12(2), 119-126.
Cherif, W. (2018). Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. Procedia Computer Science, 127, 293-299.
Cinetha, K., & Maheswari, P. U. (2014). Decision support system for precluding coronary heart disease (CHD) using fuzzy logic. IJCST, 2(2), 2347-857.
Coutinho, P. H. S., & Thiago, P. (2017, November). Proposal of new hybrid fuzzy clustering algorithms—Application to breast cancer dataset. In Computational Intelligence (LA-CCI), 2017 IEEE Latin Ameri-can Conference on (pp. 1-6). IEEE.
Das, H., Naik, B., & Behera, H. S. (2018). Classification of diabetes mellitus disease (DMD): A data min-ing (DM) approach. In Progress in Computing, Analytics and Networking (pp. 539-549). Springer, Sin-gapore.
Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert systems with applications, 36(4), 7675-7680.
Dekamin, A., & Sheibatolhamdi, A. (2017). A data mining approach for coronary artery disease predic-tion in Iran. Journal of Advanced Medical Sciences and Applied Technologies, 3(1), 29-38.
Devi, Y. N., & Anto, S. (2014). An evolutionary-fuzzy expert system for the diagnosis of coronary artery disease. IJARCET), ISSN, 2278-1323.
Einipour, A. (2011). A fuzzy-ACO method for detect breast cancer. Global journal of health sci-ence, 3(2), 195-199.
Pour, S. G., Mc Leod, P., Verma, B., & Maeder, A. (2012). Comparing data mining with ensemble classi-fication of breast cancer masses in digital mammograms. In Second Australian Workshop on Artificial Intelligence in Health: AIH, 55-63.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Hassanien, A. E., & Ali, J. M. (2004). Rough set approach for generation of classification rules of breast cancer data. Informatica, 15(1), 23-38.
Hota, H. S. (2013). Diagnosis of breast cancer using intelligent techniques. International Journal of Emerging Science and Engineering (IJESE), 1(3), 45-53.
Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2013). Heart disease classification using nearest neigh-bor classifier with feature subset selection. Anale. Seria Informatica, 11, 47-54.
Joshi, A., Dangra, J., & Rawat, M. (2016). A decision tree based classification technique for accurate heart disease classification and prediction. Int J Technol Res Manag, 3, 1-4.
Joshi, J., Doshi, R., & Patel, J. (2014). Diagnosis and prognosis breast cancer using classification rules. International Journal of Engineering Research and General Science, 2(6), 315-323.
Joshi, R., & Alehegn, M. (2017). Analysis and prediction of diabetes diseases using machine learning al-gorithm: Ensemble approach. IRJET, 4(10), 426-435.
Kadi, I., Idri, A., & Fernandez-Aleman, J. L. (2017). Knowledge discovery in cardiology: A systematic lit-erature review. International journal of medical informatics, 97, 12-32.
Kandhasamy, J. P., & Balamurali, S. (2015). Performance analysis of classifier models to predict diabetes mellitus. Procedia Computer Science, 47, 45-51.
Kaur, L. (2014). Predicting heart disease symptoms using fuzzy C-means clustering. published in Interna-tional Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Vol-ume, 3(12), 4232-4235.
Kausar, N., Abdullah, A., Samir, B. B., Palaniappan, S., AlGhamdi, B. S., & Dey, N. (2016). Ensemble clustering algorithm with supervised classification of clinical data for early diagnosis of coronary artery disease. Journal of Medical Imaging and Health Informatics, 6(1), 78-87.
Khaleel, A. H., Al-Suhail, G. A., & Hussan, B. M. (2017). A weighted voting of k-nearest neighbor algo-rithm for diabetes mellitus, 6(1), 43-51.
Kharya, S. (2012). Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv preprint arXiv:1205.1923, 2(2), 55-66.
Kim, J., Lee, J., & Lee, Y. (2015). Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthcare informatics research, 21(3), 167-174.
Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 64-72.
Krati Saxena, D., Khan, Z., & Singh, S. (2014). Diagnosis of diabetes mellitus using k-nearest neighbor al-gorithm. International Journal of Computer Science Trends and Technology (IJCST).
Kulkarni, S., Bhat, C. D., Patil, D., & Dara, J. (2018). Heart disease classification: A case study using ma-chine learning and data mining. International journal of computer trends and technology, 2(4), 36-43.
Kumar Dewangan, A., & Agrawal, P. (2015). Classification of diabetes mellitus using machine learning techniques. Int. J. Eng. Appl. Sci, 2(5), 145-148.
Kumari, M., & Godara, S. (2011). Review of data mining classification models in cardiovascular disease diagnosis. International Journal of Computer Science and Technology, 2(2), 304-305.
Kuo, W. J., Chang, R. F., Chen, D. R., & Lee, C. C. (2001). Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast cancer research and treatment, 66(1), 51-57.
Kurian, R. A., & Lakshmi, K. S. (2018). An ensemble classifier for the prediction of heart disease. Inter-national Journal of Scientific Research in Computer Science, 3(6), 25-31.
Lakshmi, K., Ahmed, D. I., & Kumar, G. S. (2018). A smart clinical decision support system to predict di-abetes disease using classification techniques. IJSRSET, 4(1), 1520-1522.
Majali, J., Niranjan, R., Phatak, V., & Tadakhe, O. (2014). Data mining techniques for diagnosis and prognosis of breast cancer. International Journal of Computer Science and Information Technologies (IJCSIT), 5(5), 6487-6490.
Malav, A., Kadam, K., & Kamat, P. (2017). Prediction of heart disease using k-means and artificial neural network as Hybrid Approach to Improve Accuracy. International Journal of Engineering and Tech-nology, 9(4), 3081-3085.
Masethe, H. D., & Masethe, M. A. (2014, October). Prediction of heart disease using classification algo-rithms. In Proceedings of the world Congress on Engineering and computer Science (Vol. 2, pp. 22-24).
MayoClinic. (2018, November). Breast cancer. Retrieved from https://www.mayoclinic.org/diseases-conditions/breast-cancer/symptoms-causes/syc-20352470?utm_source=Google&utm_medium=abstract&utm_content=Breast-cancer&utm_campaign=Knowledge-panel
MayoClinic. (2018, August). Diabetes (diseases and conditions). Retrieved from https://www.mayoclinic.org/diseases-conditions/diabetes/symptoms-causes/syc-20371444
Meng, X. H., Huang, Y. X., Rao, D. P., Zhang, Q., & Liu, Q. (2013). Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. The Kaohsiung journal of medical sci-ences, 29(2), 93-99.
Methaila, A., Kansal, P., Arya, H., & Kumar, P. (2014). Early heart disease prediction using data mining techniques. Computer Science & Information Technology Journal, 53-59.
Meza-Palacios, R., Aguilar-Lasserre, A. A., Ureña-Bogarín, E. L., Vázquez-Rodríguez, C. F., Posada-Gómez, R., & Trujillo-Mata, A. (2017). Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus. Expert Systems with Applications, 72, 335-343.
Nilashi, M., bin Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). An analytical method for diseases prediction using machine learning techniques. Computers & Chemical Engineering, 106, 212-223.
Oracle Database. (2008, July). Oracle data warehousing and business intelligence (data mining concepts). Retrieved from https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/toc.htm.
Oskouei, R. J., Kor, N. M., & Maleki, S. A. (2017). Data mining and medical world: breast cancers’ diag-nosis, treatment, prognosis and challenges. American journal of cancer research, 7(3), 610.
Palaniappan, S., & Awang, R. (2008, March). Intelligent heart disease prediction system using data mining techniques. In Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Con-ference on (pp. 108-115). IEEE.
Patel, A., Gandhi, S., Shetty, S., & Tekwani, B. (2017). Heart disease prediction using data min-ing. International Research Journal of Engineering and Technology, 4(1), 1705-1707.
Patil, R. N., & Tamane, S. C. (2018). Upgrading the performance of KNN and naïve bayes in diabetes de-tection with genetic algorithm for feature selection. International Journal of Scientific Research in Computer Science, 3(1), 1371-1381.
Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Procedia Computer Science, 82, 115-121.
Prajwala, T. R. (2015). A comparative study on decision tree and random forest using R tool. International journal of advanced research in computer and communication engineering, 4(1), 196-199.
Raad, A., Kalakech, A., & Ayache, M. (2012). Breast cancer classification using neural network ap-proach: MLP and RBF. networks, 7(8), 15-19.
Rajesh, K., & Anand, S. (2012). Analysis of SEER dataset for breast cancer diagnosis using C4.5 classifi-cation algorithm. International Journal of Advanced Research in Computer and Communication Engi-neering, 1(2), 2278-1021.
Rajkumar, A., & Reena, G. S. (2010). Diagnosis of heart disease using datamining algorithm. Global jour-nal of computer science and technology, 10(10), 38-43.
Rani, S., & Kautish, S. (2018). Application of data mining techniques for prediction of diabetes-A review. International Journal of Scientific Research in Computer Science, Engineering and Information Tech-nology, 3(3), 1996-2004.
Ratnakar, S., Rajeswari, K., & Jacob, R. (2013). Prediction of heart disease using genetic algorithm for se-lection of optimal reduced set of attributes. International Journal of Advanced Computational Engi-neering and Networking, 1(2), 51-55.
Sambyal, R. S., Javid, T., & Bansal, A. (2018). Performance analysis of data mining classification algo-rithms to Predict diabetes. International Journal of Scientific Research in Computer Science, Engineer-ing and Information Technology, 4(1), 56-63.
Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision sup-port system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Ap-plications, 68, 163-172.
Santhanam, T., & Padmavathi, M. S. (2015). Application of K-means and genetic algorithms for dimen-sion reduction by integrating SVM for diabetes diagnosis. Procedia Computer Science, 47, 76-83.
Sarvestani, A. S., Safavi, A. A., Parandeh, N. M., & Salehi, M. (2010, October). Predicting breast cancer survivability using data mining techniques. In Software technology and Engineering (ICSTE), 2010 2nd international Conference on (Vol. 2, pp. V2-227). IEEE.
Senturk, Z. K., & Kara, R. (2014). Breast cancer diagnosis via data mining: performance analysis of seven different algorithms. Computer Science & Engineering, 4(1), 35-46.
Shirwalkar, N., Gursalkar, S., Tak, T., & Kalshetti, A. (2018). Human heart disease prediction system us-ing data mining techniques. International Journal of Innovations & Advancement in Computer Science, 7(3), 357-360.
Shouman, M., Turner, T., & Stocker, R. (2011, December). Using decision tree for diagnosing heart dis-ease patients. In Proceedings of the Ninth Australasian Data Mining Conference-Volume 121 (pp. 23-30). Australian Computer Society, Inc.
Shouman, M., Turner, T., & Stocker, R. (2012, March). Using data mining techniques in heart disease di-agnosis and treatment. In Electronics, Communications and Computers (JEC-ECC), 2012 Japan-Egypt Conference on (pp. 173-177). IEEE.
Shrivastava, S. S., Sant, A., & Aharwal, R. P. (2013). An overview on data mining approach on breast cancer data. International Journal of Advanced Computer Research, 3(4), 256-262.
Shukla, N., & Arora, M. (2016). Prediction of diabetes using neural network & random forest tree. International Journal of Computer Sciences and Engineering, 4, 101-104.
Singh, N., Firozpur, P., & Jindal, S. (2018). Heart disease prediction system using hybrid technique of da-ta mining algorithms. International Journal of Advance Research, Ideas and Innovations in Technolo-gy, 4(2), 982-987.
Singh, P., Singh, S., & Pandi-Jain, G. S. (2018). effective heart disease prediction system using data min-ing techniques. International journal of nanomedicine, 13, 121-124.
Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia Com-puter Science, 132, 1578-1585.
Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.
Sumbaly, R., Vishnusri, N., & Jeyalatha, S. (2014). Diagnosis of breast cancer using decision tree data mining technique. International Journal of Computer Applications, 98(10).
Thenmozhi, K., & Deepika, P. (2014). Heart disease prediction using classification with different decision tree techniques. International Journal of Engineering Research and General Science, 2(6), 6-11.
Thirumal, P. C., & Nagarajan, N. (2015). Utilization of data mining techniques for diagnosis of diabetes mellitus-a case study. ARPN Journal of Engineering and Applied Science, 10(1), 8-13.
Tu, M. C., Shin, D., & Shin, D. (2009, October). Effective diagnosis of heart disease through bagging ap-proach. In Biomedical Engineering and Informatics, 2009. BMEI'09. 2nd International Conference on (pp. 1-4). IEEE.
Venkatalakshmi, B., & Shivsankar, M. V. (2014). Heart disease diagnosis using predictive data min-ing. International Journal of Innovative Research in Science, Engineering and Technology, 3(3), 1873-7.
Verma, L., Srivastava, S., & Negi, P. C. (2016). A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. Journal of medical systems, 40(7), 178-185.
Verma, L., & Srivastava, S. (2016). A data mining model for coronary artery disease detection using non-invasive clinical parameters. Indian Journal of Science and Technology, 9(48), 1-6.
Vijiyarani, S., & Sudha, S. (2013). An efficient classification tree technique for heart disease prediction. In International Conference on Research Trends in Computer Technologies (ICRTCT-2013) Proceed-ings published in International Journal of Computer Applications (IJCA) (0975–8887) (Vol. 201).
Wadhawan, R. (2018). Prediction of coronary heart disease using Apriori algorithm with data mining clas-sification. International Journal of Research in Science and Technology, 3(1), 1-15.
Wu, H., Yang, S., Huang, Z., He, J., & Wang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, 100-107.
Xu, W., Zhang, J., Zhang, Q., & Wei, X. (2017, February). Risk prediction of type II diabetes based on random forest model. In Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017 Third International Conference on (pp. 382-386). IEEE.
Yadav, R., Khan, Z., & Saxena, H. (2013). Chemotherapy prediction of cancer patient by using data min-ing techniques. International Journal of Computer Applications, 76(10), 28-31.
Alizadehsani, R., Habibi, J., Bahadorian, B., Mashayekhi, H., Ghandeharioun, A., Boghrati, R., & Sani, Z. A. (2012). Diagnosis of coronary arteries stenosis using data mining. Journal of medical signals and sensors, 2(3), 153-159.
Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., ... & Sani, Z. A. (2013). A data mining approach for diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 111(1), 52-61.
Anunciaçao, O., Gomes, B. C., Vinga, S., Gaspar, J., Oliveira, A. L., & Rueff, J. (2010). A data mining approach for the detection of high-risk breast cancer groups. In Advances in Bioinformatics (pp. 43-51). Springer, Berlin, Heidelberg.
Babu, S., Vivek, E. M., Famina, K. P., Fida, K., Aswathi, P., Shanid, M., & Hena, M. (2017, April). Heart disease diagnosis using data mining technique. In Electronics, Communication and Aerospace Tech-nology (ICECA), 2017 International conference of (Vol. 1, pp. 750-753). IEEE.
Baihaqi, W. M., Setiawan, N. A., & Ardiyanto, I. (2016, August). Rule extraction for fuzzy expert system to diagnose coronary artery disease. In Information Technology, Information Systems and Electrical Engineering (ICITISEE), International Conference on (pp. 136-141). IEEE.
Bellaachia, A., & Guven, E. (2006). Predicting breast cancer survivability using data mining tech-niques. Age, 58(13), 10-110.
Bhargava, N., Dayma, S., Kumar, A., & Singh, P. (2017, January). An approach for classification using simple CART algorithm in WEKA. In Intelligent Systems and Control (ISCO), 2017 11th International Conference on (pp. 212-216). IEEE.
Bhatla, N., & Jyoti, K. (2012). An analysis of heart disease prediction using different data mining tech-niques. International Journal of Engineering, 1(8), 1-4.
Burke, H. B., Goodman, P. H., Rosen, D. B., Henson, D. E., Weinstein, J. N., Harrell Jr, F. E., ... & Bostwick, D. G. (1997). Artificial neural networks improve the accuracy of cancer survival predic-tion. Cancer, 79(4), 857-862.
Chang, W. P., & Liou, D. M. (2008). Comparison of three data mining techniques with genetic algorithm in the analysis of breast cancer data. J Telemed Telecare, 9(1), 26.
Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology, 12(2), 119-126.
Cherif, W. (2018). Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. Procedia Computer Science, 127, 293-299.
Cinetha, K., & Maheswari, P. U. (2014). Decision support system for precluding coronary heart disease (CHD) using fuzzy logic. IJCST, 2(2), 2347-857.
Coutinho, P. H. S., & Thiago, P. (2017, November). Proposal of new hybrid fuzzy clustering algorithms—Application to breast cancer dataset. In Computational Intelligence (LA-CCI), 2017 IEEE Latin Ameri-can Conference on (pp. 1-6). IEEE.
Das, H., Naik, B., & Behera, H. S. (2018). Classification of diabetes mellitus disease (DMD): A data min-ing (DM) approach. In Progress in Computing, Analytics and Networking (pp. 539-549). Springer, Sin-gapore.
Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert systems with applications, 36(4), 7675-7680.
Dekamin, A., & Sheibatolhamdi, A. (2017). A data mining approach for coronary artery disease predic-tion in Iran. Journal of Advanced Medical Sciences and Applied Technologies, 3(1), 29-38.
Devi, Y. N., & Anto, S. (2014). An evolutionary-fuzzy expert system for the diagnosis of coronary artery disease. IJARCET), ISSN, 2278-1323.
Einipour, A. (2011). A fuzzy-ACO method for detect breast cancer. Global journal of health sci-ence, 3(2), 195-199.
Pour, S. G., Mc Leod, P., Verma, B., & Maeder, A. (2012). Comparing data mining with ensemble classi-fication of breast cancer masses in digital mammograms. In Second Australian Workshop on Artificial Intelligence in Health: AIH, 55-63.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Hassanien, A. E., & Ali, J. M. (2004). Rough set approach for generation of classification rules of breast cancer data. Informatica, 15(1), 23-38.
Hota, H. S. (2013). Diagnosis of breast cancer using intelligent techniques. International Journal of Emerging Science and Engineering (IJESE), 1(3), 45-53.
Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2013). Heart disease classification using nearest neigh-bor classifier with feature subset selection. Anale. Seria Informatica, 11, 47-54.
Joshi, A., Dangra, J., & Rawat, M. (2016). A decision tree based classification technique for accurate heart disease classification and prediction. Int J Technol Res Manag, 3, 1-4.
Joshi, J., Doshi, R., & Patel, J. (2014). Diagnosis and prognosis breast cancer using classification rules. International Journal of Engineering Research and General Science, 2(6), 315-323.
Joshi, R., & Alehegn, M. (2017). Analysis and prediction of diabetes diseases using machine learning al-gorithm: Ensemble approach. IRJET, 4(10), 426-435.
Kadi, I., Idri, A., & Fernandez-Aleman, J. L. (2017). Knowledge discovery in cardiology: A systematic lit-erature review. International journal of medical informatics, 97, 12-32.
Kandhasamy, J. P., & Balamurali, S. (2015). Performance analysis of classifier models to predict diabetes mellitus. Procedia Computer Science, 47, 45-51.
Kaur, L. (2014). Predicting heart disease symptoms using fuzzy C-means clustering. published in Interna-tional Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Vol-ume, 3(12), 4232-4235.
Kausar, N., Abdullah, A., Samir, B. B., Palaniappan, S., AlGhamdi, B. S., & Dey, N. (2016). Ensemble clustering algorithm with supervised classification of clinical data for early diagnosis of coronary artery disease. Journal of Medical Imaging and Health Informatics, 6(1), 78-87.
Khaleel, A. H., Al-Suhail, G. A., & Hussan, B. M. (2017). A weighted voting of k-nearest neighbor algo-rithm for diabetes mellitus, 6(1), 43-51.
Kharya, S. (2012). Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv preprint arXiv:1205.1923, 2(2), 55-66.
Kim, J., Lee, J., & Lee, Y. (2015). Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthcare informatics research, 21(3), 167-174.
Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 64-72.
Krati Saxena, D., Khan, Z., & Singh, S. (2014). Diagnosis of diabetes mellitus using k-nearest neighbor al-gorithm. International Journal of Computer Science Trends and Technology (IJCST).
Kulkarni, S., Bhat, C. D., Patil, D., & Dara, J. (2018). Heart disease classification: A case study using ma-chine learning and data mining. International journal of computer trends and technology, 2(4), 36-43.
Kumar Dewangan, A., & Agrawal, P. (2015). Classification of diabetes mellitus using machine learning techniques. Int. J. Eng. Appl. Sci, 2(5), 145-148.
Kumari, M., & Godara, S. (2011). Review of data mining classification models in cardiovascular disease diagnosis. International Journal of Computer Science and Technology, 2(2), 304-305.
Kuo, W. J., Chang, R. F., Chen, D. R., & Lee, C. C. (2001). Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast cancer research and treatment, 66(1), 51-57.
Kurian, R. A., & Lakshmi, K. S. (2018). An ensemble classifier for the prediction of heart disease. Inter-national Journal of Scientific Research in Computer Science, 3(6), 25-31.
Lakshmi, K., Ahmed, D. I., & Kumar, G. S. (2018). A smart clinical decision support system to predict di-abetes disease using classification techniques. IJSRSET, 4(1), 1520-1522.
Majali, J., Niranjan, R., Phatak, V., & Tadakhe, O. (2014). Data mining techniques for diagnosis and prognosis of breast cancer. International Journal of Computer Science and Information Technologies (IJCSIT), 5(5), 6487-6490.
Malav, A., Kadam, K., & Kamat, P. (2017). Prediction of heart disease using k-means and artificial neural network as Hybrid Approach to Improve Accuracy. International Journal of Engineering and Tech-nology, 9(4), 3081-3085.
Masethe, H. D., & Masethe, M. A. (2014, October). Prediction of heart disease using classification algo-rithms. In Proceedings of the world Congress on Engineering and computer Science (Vol. 2, pp. 22-24).
MayoClinic. (2018, November). Breast cancer. Retrieved from https://www.mayoclinic.org/diseases-conditions/breast-cancer/symptoms-causes/syc-20352470?utm_source=Google&utm_medium=abstract&utm_content=Breast-cancer&utm_campaign=Knowledge-panel
MayoClinic. (2018, August). Diabetes (diseases and conditions). Retrieved from https://www.mayoclinic.org/diseases-conditions/diabetes/symptoms-causes/syc-20371444
Meng, X. H., Huang, Y. X., Rao, D. P., Zhang, Q., & Liu, Q. (2013). Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. The Kaohsiung journal of medical sci-ences, 29(2), 93-99.
Methaila, A., Kansal, P., Arya, H., & Kumar, P. (2014). Early heart disease prediction using data mining techniques. Computer Science & Information Technology Journal, 53-59.
Meza-Palacios, R., Aguilar-Lasserre, A. A., Ureña-Bogarín, E. L., Vázquez-Rodríguez, C. F., Posada-Gómez, R., & Trujillo-Mata, A. (2017). Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus. Expert Systems with Applications, 72, 335-343.
Nilashi, M., bin Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). An analytical method for diseases prediction using machine learning techniques. Computers & Chemical Engineering, 106, 212-223.
Oracle Database. (2008, July). Oracle data warehousing and business intelligence (data mining concepts). Retrieved from https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/toc.htm.
Oskouei, R. J., Kor, N. M., & Maleki, S. A. (2017). Data mining and medical world: breast cancers’ diag-nosis, treatment, prognosis and challenges. American journal of cancer research, 7(3), 610.
Palaniappan, S., & Awang, R. (2008, March). Intelligent heart disease prediction system using data mining techniques. In Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Con-ference on (pp. 108-115). IEEE.
Patel, A., Gandhi, S., Shetty, S., & Tekwani, B. (2017). Heart disease prediction using data min-ing. International Research Journal of Engineering and Technology, 4(1), 1705-1707.
Patil, R. N., & Tamane, S. C. (2018). Upgrading the performance of KNN and naïve bayes in diabetes de-tection with genetic algorithm for feature selection. International Journal of Scientific Research in Computer Science, 3(1), 1371-1381.
Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Procedia Computer Science, 82, 115-121.
Prajwala, T. R. (2015). A comparative study on decision tree and random forest using R tool. International journal of advanced research in computer and communication engineering, 4(1), 196-199.
Raad, A., Kalakech, A., & Ayache, M. (2012). Breast cancer classification using neural network ap-proach: MLP and RBF. networks, 7(8), 15-19.
Rajesh, K., & Anand, S. (2012). Analysis of SEER dataset for breast cancer diagnosis using C4.5 classifi-cation algorithm. International Journal of Advanced Research in Computer and Communication Engi-neering, 1(2), 2278-1021.
Rajkumar, A., & Reena, G. S. (2010). Diagnosis of heart disease using datamining algorithm. Global jour-nal of computer science and technology, 10(10), 38-43.
Rani, S., & Kautish, S. (2018). Application of data mining techniques for prediction of diabetes-A review. International Journal of Scientific Research in Computer Science, Engineering and Information Tech-nology, 3(3), 1996-2004.
Ratnakar, S., Rajeswari, K., & Jacob, R. (2013). Prediction of heart disease using genetic algorithm for se-lection of optimal reduced set of attributes. International Journal of Advanced Computational Engi-neering and Networking, 1(2), 51-55.
Sambyal, R. S., Javid, T., & Bansal, A. (2018). Performance analysis of data mining classification algo-rithms to Predict diabetes. International Journal of Scientific Research in Computer Science, Engineer-ing and Information Technology, 4(1), 56-63.
Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision sup-port system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Ap-plications, 68, 163-172.
Santhanam, T., & Padmavathi, M. S. (2015). Application of K-means and genetic algorithms for dimen-sion reduction by integrating SVM for diabetes diagnosis. Procedia Computer Science, 47, 76-83.
Sarvestani, A. S., Safavi, A. A., Parandeh, N. M., & Salehi, M. (2010, October). Predicting breast cancer survivability using data mining techniques. In Software technology and Engineering (ICSTE), 2010 2nd international Conference on (Vol. 2, pp. V2-227). IEEE.
Senturk, Z. K., & Kara, R. (2014). Breast cancer diagnosis via data mining: performance analysis of seven different algorithms. Computer Science & Engineering, 4(1), 35-46.
Shirwalkar, N., Gursalkar, S., Tak, T., & Kalshetti, A. (2018). Human heart disease prediction system us-ing data mining techniques. International Journal of Innovations & Advancement in Computer Science, 7(3), 357-360.
Shouman, M., Turner, T., & Stocker, R. (2011, December). Using decision tree for diagnosing heart dis-ease patients. In Proceedings of the Ninth Australasian Data Mining Conference-Volume 121 (pp. 23-30). Australian Computer Society, Inc.
Shouman, M., Turner, T., & Stocker, R. (2012, March). Using data mining techniques in heart disease di-agnosis and treatment. In Electronics, Communications and Computers (JEC-ECC), 2012 Japan-Egypt Conference on (pp. 173-177). IEEE.
Shrivastava, S. S., Sant, A., & Aharwal, R. P. (2013). An overview on data mining approach on breast cancer data. International Journal of Advanced Computer Research, 3(4), 256-262.
Shukla, N., & Arora, M. (2016). Prediction of diabetes using neural network & random forest tree. International Journal of Computer Sciences and Engineering, 4, 101-104.
Singh, N., Firozpur, P., & Jindal, S. (2018). Heart disease prediction system using hybrid technique of da-ta mining algorithms. International Journal of Advance Research, Ideas and Innovations in Technolo-gy, 4(2), 982-987.
Singh, P., Singh, S., & Pandi-Jain, G. S. (2018). effective heart disease prediction system using data min-ing techniques. International journal of nanomedicine, 13, 121-124.
Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia Com-puter Science, 132, 1578-1585.
Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.
Sumbaly, R., Vishnusri, N., & Jeyalatha, S. (2014). Diagnosis of breast cancer using decision tree data mining technique. International Journal of Computer Applications, 98(10).
Thenmozhi, K., & Deepika, P. (2014). Heart disease prediction using classification with different decision tree techniques. International Journal of Engineering Research and General Science, 2(6), 6-11.
Thirumal, P. C., & Nagarajan, N. (2015). Utilization of data mining techniques for diagnosis of diabetes mellitus-a case study. ARPN Journal of Engineering and Applied Science, 10(1), 8-13.
Tu, M. C., Shin, D., & Shin, D. (2009, October). Effective diagnosis of heart disease through bagging ap-proach. In Biomedical Engineering and Informatics, 2009. BMEI'09. 2nd International Conference on (pp. 1-4). IEEE.
Venkatalakshmi, B., & Shivsankar, M. V. (2014). Heart disease diagnosis using predictive data min-ing. International Journal of Innovative Research in Science, Engineering and Technology, 3(3), 1873-7.
Verma, L., Srivastava, S., & Negi, P. C. (2016). A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. Journal of medical systems, 40(7), 178-185.
Verma, L., & Srivastava, S. (2016). A data mining model for coronary artery disease detection using non-invasive clinical parameters. Indian Journal of Science and Technology, 9(48), 1-6.
Vijiyarani, S., & Sudha, S. (2013). An efficient classification tree technique for heart disease prediction. In International Conference on Research Trends in Computer Technologies (ICRTCT-2013) Proceed-ings published in International Journal of Computer Applications (IJCA) (0975–8887) (Vol. 201).
Wadhawan, R. (2018). Prediction of coronary heart disease using Apriori algorithm with data mining clas-sification. International Journal of Research in Science and Technology, 3(1), 1-15.
Wu, H., Yang, S., Huang, Z., He, J., & Wang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, 100-107.
Xu, W., Zhang, J., Zhang, Q., & Wei, X. (2017, February). Risk prediction of type II diabetes based on random forest model. In Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017 Third International Conference on (pp. 382-386). IEEE.
Yadav, R., Khan, Z., & Saxena, H. (2013). Chemotherapy prediction of cancer patient by using data min-ing techniques. International Journal of Computer Applications, 76(10), 28-31.