Classification and Prediction of Heart Diseases using Machine Learning Algorithms
This work addresses the need for reliable and cost-effective heart disease prediction tools in the medical field, but it is incremental as it compares existing methods on a standard dataset.
The study aimed to identify the best machine learning classifier for predicting heart diseases, testing algorithms like Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks on the UCI heart disease dataset, and found that K-Nearest Neighbor was the most effective.
Heart disease is a serious worldwide health issue because it claims the lives of many people who might have been treated if the disease had been identified earlier. The leading cause of death in the world is cardiovascular disease, usually referred to as heart disease. Creating reliable, effective, and precise predictions for these diseases is one of the biggest issues facing the medical world today. Although there are tools for predicting heart diseases, they are either expensive or challenging to apply for determining a patient's risk. The best classifier for foretelling and spotting heart disease was the aim of this research. This experiment examined a range of machine learning approaches, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks, to determine which machine learning algorithm was most effective at predicting heart diseases. One of the most often utilized data sets for this purpose, the UCI heart disease repository provided the data set for this study. The K-Nearest Neighbor technique was shown to be the most effective machine learning algorithm for determining whether a patient has heart disease. It will be beneficial to conduct further studies on the application of additional machine learning algorithms for heart disease prediction.