Recent Papers

Heart disease is a leading cause of mortality worldwide, emphasizing the need for accurate and timely diagnosis. Machine learning techniques have shown promise in predicting heart disease risk, and this study focuses on the application of the Machine Learning algorithms for heart disease prediction.We explore a wide range of machine learning techniques, including decision trees, support vector machines, random forests, and K-Nearest Neighbour to assess their performance in heart disease prediction. Our study involves data preprocessing, feature selection, and model evaluation to ensure the robustness and accuracy of the predictions. The results reveal promising outcomes in terms of predictive accuracy, specificity. We compare the performance of these algorithms and discuss their strengths and limitations in the context of heart disease prediction. In this system, to train and test the Machine Learning algorithms, we use a dataset containing clinical and demographic data. Using these algorithms, we divide patients into two categories: those who are at risk of heart disease and those who are not.

Copyright © 2023 Aniket Navale. This is an open access article distributed under the Creative Commons Attribution License.