Neural Network Optimization Using Hybrid Adaptive Mutation Particle Swarm Optimization and Levenberg-Marquardt in Cases of Cardiovascular Disease - Cahyani, Rima Ayu; Purwinarko, Aji (Recursive Journal of Informatics, 2024)
Abstract. Cardiovascular disease is a condition generally characterized by the narrowing or blockage of blood vessels, which can lead to heart attacks, chest pain, or strokes. It is the leading cause of death worldwide, accounting for approximately 31% or 17.9 million deaths each year globally. Deaths caused by cardiovascular disease are projected to continue increasing until 2030, with the number of patients reaching 23.3 million. As cases of death due to cardiovascular disease become more prevalent, early detection is crucial to reduce mortality rates. Purpose: Many previous researchers have conducted studies on predicting cardiovascular disease using neural network methods. This study extends these methods by incorporating feature selection and optimization with Hybrid AMPSO-LMA. The research is designed to explore the implementation and predictive outcomes of Hybrid AMPSO-LMA in optimizing MLP for cases of cardiovascular disease. Methods/Study design/approach: The first step in conducting this research is to download the Heart Disease Dataset from Kaggle.com. The dataset is processed through preprocessing by removing duplicates and transforming the data. Then, data mining processes are carried out using the MLP algorithm optimized with Hybrid AMPSO-LMA to obtain results and conclusions. This system is designed using the Python programming language and utilizes Flask for website access in HTML. Result/Findings: The research results demonstrate that the method employed by the author successfully improves the accuracy of predicting cardiovascular disease. Predicting cardiovascular disease using the MLP algorithm yields an accuracy of 86.1%, and after optimization with Hybrid AMPSO-LMA, the accuracy increases to 86.88%. Novelty/Originality/Value: This effort will contribute to the development of a more reliable and effective cardiovascular disease prediction system, with the goal of early identification of individuals exhibiting symptoms of cardiovascular disease.