Ensemble Approach for Heart Disease Diagnosis Integrating HHO

What is it about?

Heart disease diagnosis is a crucial task in healthcare, and this paper proposes an ensemble method that combines the Harris Hawks Optimization (HHO) algorithm with machine learning techniques to improve accuracy. The HHO algorithm optimizes feature selection, reducing dimensionality and enhancing classification performance. An ensemble model incorporates several machine learning classifiers, such as neural networks, decision trees, and support vector machines. The ensemble model using HHO-based feature selection performs better than independent machine learning models using a large dataset containing clinical and demographic data. Comparative studies show that the ensemble method produces more robust and accurate diagnoses. Additionally, it permits the study of feature importance, providing valuable information on critical diagnostic parameters related to heart disease. Enhancing feature selection efficiency, lowering the likelihood of overfitting, and improving interpretability are all achieved by incorporating the HHO method with the ensemble model. With an F1 score of 96.18%, accuracy, sensitivity, precision, recall, and 94.35%, 95.21%, 95.26%, and 95.18%, respectively, the experimental findings show the efficacy of the suggested approach. By offering a potent instrument for precise identification of heart disease, this ensemble approach helps medical professionals make better decisions and maybe enhance patient outcomes. Prospective investigations may delve into substitute optimization algorithms and expand ensemble approaches to additional medical diagnostic assignments, so creating novel avenues for the improvement of cardiovascular health.

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Photo by Steve Johnson on Unsplash

Photo by Steve Johnson on Unsplash

Why is it important?

In this work, we minimise dimensionality and optimise feature selection by integrating the HHO method into the ensemble model. This method enhances the model's precision and comprehensibility. Our objective is to create a predictive model that is more useful and comprehensible and offers insightful clinical information.