Harnessing Statistical Analysis and Machine Learning Optimization for Heart Attack Prediction

Abstract

Artificial intelligence (AI) and the decisions derived from data are pivotal arms in almost any field nowadays; in consequence, statistical analysis and machine learning (ML) have been used in biomedical research, especially in the early diagnosis of complicated disorders such as heart diseases. Early-stage prediction of cardiovascular disease can guide physicians towards better and early treatment and improved outcomes. Here, we utilize ML tools for the enhancement of clinical decision-making based on the digital metadata of the patient. The dataset comes from UCI repository and Applied methods include descriptive statistics for better understanding, correlation/covariance to tell if there is a relationship between explanatory features such as chest pain and target values, classification analysis to explore the disease and evaluate the model, Risk factor analysis to mark the most significant inputs for algorithms, clustering techniques to group patients with similar profiles, logistic regression (LR) and comparing the results after optimization using grid search method, K-means clustering, random forest (RF), ROC curve and AUC to assess the model's ability to diagnose patients. Then applying perceptron algorithm. Suitably, with simple ML algorithms, we can predict the prognosis of the disease with high accuracy