Swarm Intelligence Driven Early Prediction of Heart Disease: A Novel Machine Learning Approach

Abstract

In recent years, machine learning (ML) models have become the top prediction options in agriculture, cybersecurity, healthcare, and finance, among other fields. ML models have assisted in the study of numerous disorders in medical research. Early diagnosis and health schedule management may prevent severe heart disease in many people. This study uses national government repository data to apply ML models to early heart disease detection. This research also introduces two novel models based on swarm intelligence, called ACOKNN (ant colony optimization with K-nearest neighbors) and ACOWNNBoost (ant colony optimization with weighted K-nearest neighbors), to improve heart disease early detection models. The suggested models pick features using ant colony optimization with traditional k-nearest neighbor and weighted k-nearest neighbor with XGBoost. The suggested models are evaluated with two datasets of varying capacity, and results are compared to various ML techniques based on precision, accuracy, recall, F1 score, and receiver operating characteristic area under the curve score. The investigation shows that the suggested model achieved 92% and 98% accuracy with Dataset-1 and 92.6% and 98.6% with Dataset-2.