Enhancing the Heart Disease Classification Using Multi-level Perceptron and Principal Component Analysis

The ability to detect cardiac disease early is vital to saving lives. Heart attacks are one of the leading reasons for high death rates worldwide due to the high cost of identifying cardiac disorders, which is crucial to the healthcare industry, as well as the shortage of human and logistical resources. In order to find the best machine learning algorithm for early-stage heart disease prediction, a model is presented in this study. In order to train and evaluate the model based on the data for efficient decision-making, machine learning techniques are utilized. The goal of this research is to combine Principal Component Analysis and Multilevel Perceptron model to propose an updated diagnosis and classification model based on machine learning (ML) for predicting heart failure and heart disease detection. Using cutting-edge techniques and foundational classifiers like Support Vector machines, Random forests, and K-Nearest Neighbors, an empirical evaluation of the suggested methodology was carried out with accuracy, precision, recall, and F1-score serving as the evaluation criteria. The proposed method has shown the maximum accuracy of 95.1%. This innovative approach not only streamlines the diagnostic process but also offers personalized medical assistance, facilitating early detection and intervention in heart disease, thereby optimizing healthcare delivery and improving patient outcomes.