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@article{184780,
        author = {Prof. Rakesh Ramesh Tannu},
        title = {Predicting Heart Diseases Using Machine Learning and Different Data Classification Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {257-267},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184780},
        abstract = {Heart disease (HD), including heart attacks, is a primary cause of death across the world. In the area of medical data analysis, one of the most difficult problems to solve is determining the probability of a patient having heart disease. Mortality rates can be reduced through early detection of heart diseases and continuous monitoring of patients by doctors Regretfully, a doctor cannot always be in contact with a patient, and heart illness cannot always be appropriately diagnosed. By offering a more accurate foundation for forecasting and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) holds promise for assisting in diagnosis. In order to create a precise machine learning strategy for predicting heart disease in its early stages, this study will use a number of feature selection techniques. Three different techniques—chi-square, analysis of variance (ANOVA), and mutual information (MI)—were used in the feature selection procedure. The three feature groups that were ultimately selected were referred to as SF-1, SF-2, and SF-3, respectively. Then, ten different ML classifiers were used to determine the best technique, and which feature subset was the greatest fit. These classifiers included Naive Bayes, support vector machine (SVM), voting, XGBoost, AdaBoost, bagging, decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), and they were denoted as (A1, A2, . . . , A10). The proposed approach for predicting heart diseases was evaluated using a private dataset, a publicly available dataset, and multiple cross-validation methods To find the classifier that generates the best rate of accurate heart disease predictions, we applied the Synthetic Minority Oversampling Technique (SMOTE) to fix the issue of unbalanced data. To comprehend how the system forecasts its final outcomes, an explainable artificial intelligence strategy utilizing SHAP techniques is being developed. With its low cost and short turnaround time, the suggested method showed tremendous potential for the healthcare industry in predicting early-stage cardiac disease. In the end, the most effective machine learning technique was applied to create a mobile application that enables users to input HD symptoms and promptly obtain a heart disease forecast. The proposed technique had great promise for the healthcare sector to predict early-stage heart disease with cheap cost and minimal time. Ultimately, the best ML method has been used to make a mobile app that lets users enter HD symptoms and quickly receive a heart disease prediction.},
        keywords = {heart disease, machine learning app, ML algorithms Cardiovascular disease SDG 3, SHAP, SMOTE.},
        month = {September},
        }