AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING

Cardiovascular diseases (CD) are the common cause of death worldwide over in developed as well as underdeveloped and developing countries. Early detection and continuous supervision can reduce the mortality rate. Cardiovascular disease diagnosis and accurate diagnosis to enable early treatment. Some of these techniques do not easily diagnose heart diseases at early stages hence, getting treatment late poses a big risk. The present work attempts to better predict this disease from the chest pain symptom, and classify it by designing an efficient machine learning system based on a dataset with 303 patient data made available to the public domain. The four machine learning algorithms that were used for the analysis include Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks to determine which of them is most appropriate for predicting heart diseases. Original data was preprocessed by handling missing values, normalizing features, and using feature extraction techniques. Splitting the dataset into 80% training and 20% testing, cross-validation was performed to validate outcomes on all four models. Although the highest accuracy was reached by the model of th