Predicting the onset of Herat Failure using ML algorithm and Deploying it in an app.
Heart failure is a serious medical condition that affects millions of people worldwide and leads to high mortality rates. Early prediction of heart failure can greatly improve patient outcomes and reduce healthcare costs. Machine learning techniques have shown promising results in predicting heart failure. This paper presents a review of various machine learning algorithms that have been used to predict heart failure, including decision trees, random forests, k-nearest neighbors, artificial neural networks, and support vector machines. The performance of each algorithm is compared and evaluated using various metrics such as accuracy, sensitivity, and specificity. The results show that machine learning algorithms can effectively predict heart failure and can be used as a valuable tool for healthcare professionals in the early diagnosis and treatment of this condition. The paper concludes with a discussion of the limitations of the current studies and the future directions for the use of machine learning in heart failure prediction. The authors emphasize the need for further research to validate the results and to improve the performance of machine learning algorithms in predicting heart failure. It is also important to consider the ethical and privacy implications of using machine learning algorithms in clinical practice.
