Diagnosis of Human Heart Ailment using GridSearchCV

Nowadays, heart disease can affect most of the people in the world. ECG technology are used to estimate and diagnosis the heart diseases in the early days in the hospitals. Predicting this cardiac disease is a most challenging task in the medical field. Finding this problem will requires a lot of time and work. This paper deals with the different Machine learning techniques to detect and diagnosis the heart disease. In this project, we use Machine learning algorithms such as Logistic regression (LR), K-nearest neighbors (KNN), Support vector Machine (SVM) to predict and diagnosis the heart disease. As a result, it is simple to handle the patients more successfully and also on the other hand reduce the error. In this study, we work on the GridSearchCV technique to predicting the cardiac diseases and improving the accuracy of the predicted model. At last, we use evaluation metrics like precision, accuracy and recall for the above-mentioned Machine learning algorithms based on evaluation GridSearchCV to enhance the quality of the prediction of models which leads to more accurate detection of heart disease.