Heart Disease Prediction using Machine Learning and Data Mining
Keshav Srivastava1, Dilip Kumar Choubey2
1Keshav Srivastava, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
2Dilip Kumar Choubey*, School of Computer Science andEngineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on April 04, 2020. | Revised Manuscript received on April 13, 2020. | Manuscript published on May 30, 2020. | PP: 212-219 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1340059120/2020©BEIESP | DOI: 10.35940/ijrte.F9199.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Weighing only 300 grams, Heart is declining the mortality rate at a rapid pace from decades. The major factors that contribute to it are smoking, drinking, unbalanced diet, and many more. Even with these more technical advancements the analysis of the clinical data is a critical challenge. With the use of Machine Learning techniques, it is possible to analyse the data and interpret the cause that lead to heart diseases such as Coronary Heart Disease, Arrhythmia, and Dilated Cardiomyopathy. Many researchers are developing IoT enabled hardware to predict these diseases using various ML and DM techniques. In this study, we propose a novel method to determine the disease using Cleveland Heart Disease Dataset by combining the computational power of various ML and DM algorithms and concluded that among all the algorithms, K-Nearest Neighbors gives the highest accuracy of 87%. Along with this, a web app is developed using flask in python with which the user can enter the attributes and predict the heart disease.
Keywords: Classification, Data Mining, Decision Trees, Heart Disease, KNN, Logistic Regression, Machine Learning, Naïve Bayes, Random Forest, SVM.
Scope of the Article: Machine Learning