Algorithm Accuracy Verification in Heart Disease Analysis using Machine Learning

Recent studies say that heart diseases are the major threat to humans. The diagnosis of the disease is obtained by making predictions from the patient's medical details. A minor error in predicting or diagnosis the results of heart related diseases can cause several problems. To address the issue, several researchers used the hospital data or patients' information for data mining and statistical tools for helping the health care system in the diagnosis of heart diseases. For making people aware of heart disease, a prediction model is required for early detection. The prediction model uses the training data and predicts the results by using several machine learning techniques. Using this training data, the testing of the other data is done precisely. In this research, for the prediction of the results from the given data, machine learning algorithms are used for model development. The prediction includes accuracy of each algorithm. By using machine learning techniques, the correlation between various features present in the dataset is also identified in the research while performing the experiment. The framework makes use of 13 features, including ones related to age, gender, obesity, blood pressure, cholesterol, and cp as various attributes to generate the classifiers. Using these features the output of these classifiers reveals that the accuracy of each algorithm and assists in predicting risk factors related to heart diseases and gives which is best suitable technique for producing the best predictions.