By Pooja Toshniwal PahariaApr 5 2024Reviewed by Lily Ramsey, LLM In a recent study published in Scientific Reports, researchers developed a machine learning-based heart disease prediction model that uses various combinations of information and numerous recognized categorization methods.
The proposed approaches use modern technology and feature selection procedures to enhance heart disease diagnosis and prognosis. The genetic algorithm comprised population initialization, selection, crossover, and mutation to determine if the termination criterion was satisfied. Model classifiers were principal component analysis , support vector machine , linear discriminant analysis , decision tree , random forest , and naïve Bayes .
Results ML-HDPM performed admirably over a wide range of critical evaluation criteria, as evidenced by the comprehensive examination. Using training data, the ML-HDPM model predicted cardiovascular disease with 96% accuracy and 95% precision. Feature selection algorithms enable finding significant qualities associated with cardiovascular health, allowing them to detect subtle patterns indicative of cardiovascular disease.