A Meta-Heuristic Approach for Optimizing Neural Network Model for Heart Disease Prediction

In heart-disease detection, we have several systems utilizing Machine Learning models to assess the likelihood of a person experiencing heart disease based on input data gathered from health checkups. To deploy these in real-time, we need to constantly update the model with validation data generated from the feedback of doctors and users. One crucial component is hyperparameter tuning, as hyperparameters define the working conditions of a model. Current hyperparameter tuning methods have more time-complexity, which might cause latency. Meta-heuristic algorithms are known to achieve optimal solutions in less time, leveraging accuracy. In this paper, we performed time-complexity analysis for three bio-inspired meta-heuristic algorithms viz. Genetic Algorithm, Ant-Colony Optimization and Swarm-bee optimization for hyperparameter tuning in the context of Artificial Neural Networks in heart disease prediction. We achieved accuracy 93.17 on Cleveland dataset and 95.12 on Cleveland, Hungary, Switzerland, and Long Beach V combined dataset, which outperformed conventional algorithms in less time complexity. We built a web-app and mobile-app based on best models.