An Application of Artificial Intelligence for an Early and Effective Prediction of Heart Failure

The purpose of this study is to develop a reliable decision support system for predicting the survival of heart failure patients. Over time, heart disease (CVD) has become one of the most visible diseases in the world. The major factors of Heart failure are Sex, cholesterol, high blood pressure, stress, age, Exercise Angina, and Resting ECG. Many researchers have proposed several methods for early diagnosis on the bases of these features. However, due to the hereditary critique of heart disease and the life-threatening risks, it is important to improve the accuracy of the proposed techniques and methods. In this article, a machine learning framework with high accuracy is proposed for the effective diagnosis of heart failure. Specifically, the framework deals with handling missing values through the first Example filter. In the second stage, the data imbalance problem is solved through the Synthetic Minority Over-sampling Technique (SMOTE Upsampling). In the third step, the feature selection is done using (Optimized Feature Selection). The fourth is to normalize the data using the normalization technique, the fifth is to split the data into portions using split operators (30% and 70 ^% ). In the final step, the Decision Tree and K-Nearest Neighbor (KNN) classifiers are introduced for effective forecasting as these classifiers achieve the best accuracy (84.11%). The dataset validation has been performed in the background using four types of datasets. (i.e. Failure Prediction Dataset, Cardiovascular Disease, Stroke Prediction Dataset, heart disease). Comparative analysis proves that (Heart Failure Prediction) Dataset achieves better accuracy (84.11%) with fewer sets of features.