Jellyfish Algorithm for Feature Selection: Improving Machine Learning-Based Heart Disease Prediction

Abstract

This research paper describes a sophisticated medical diagnosis system based on machine learning (ML) to predict heart disease. The Jellyfish algorithm optimizes the Cleveland dataset, which aims to achieve the most accurate predictions with the most significant features chosen. The selection of features is crucial for performance as there can be excessive features causing overfitting or too few features causing accuracy loss. The jellyfish technique is type of swarm metaheuristic approach in which the feature selection is optimized and performance of the model is enhanced. After selecting features, we will train four machine learning algorithms on the optimized dataset and program. The algorithms are Artificial Neural Networks (ANN), Decision Tree (DT), AdaBoost, and Support Vector Machines (SVM) Results show that every single model benefits from feature selection. The feature selection mostly impacts the Support Vector Machine model which sees the highest increase from 98.09% to 98.47%. Every performance metric has a respective enhancement in Sensitivity, Specificity and Area under the Curve (AUC) which shows that Jellyfish can enhance the accuracy of heart diseases prediction.