Automated heart disease prediction model by hybrid heuristic-based feature optimization and enhanced clustering

Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm algorithm and stacking classifier

Despite technological advancements, heart disease continues to be a major global health challenge, emphasizing the importance of developing accurate predictive models for early detection and timely intervention. This study proposes a heart disease prediction model integrating a stacking classifier with a nature-inspired meta-heuristic algorithm. It employs an improved Binary Salp Swarm Algorithm (BSSA) by incorporating a wolf optimizer and opposition-based learning for optimal feature selection. The proposed Stacking Classifier (SC) architecture features a two-tier ensemble: heterogeneous base classifiers at level 0 and a meta-learner at level 1. The BSSA is used to identify optimal features, which are then utilized to construct the stacking classifier. Experimental results demonstrate superior performance, achieving 95 % accuracy, 0.92 sensitivity, 0.97 specificity, 0.96 precision, and an F1 score of 0.95, with notably low false positive and false negative rates. Further, validation on larger datasets yielded an accuracy of 87.46 %. The feature selection process adopts a multi-objective strategy which enhances the classification accuracy and outperforms conventional techniques. The proposed method demonstrates significant potential for improving the predictive modelling in clinical settings for diagnosing heart diseases.

Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron

Cardiac disease refers to diseases that affect the heart such as coronary artery diseases, arrhythmia and heart defects and is amongst the most difficult health conditions known to humanity. According to the WHO, heart disease is the foremost cause of mortality worldwide, causing an estimated 17.8 million deaths every year it consumes a significant amount of time as well as effort to figure out what is causing this, especially for medical specialists and doctors. Manual methods for detecting cardiac disease are biased and subject to medical specialist variance. In this aspect, machine learning algorithms have proved to be effective and dependable alternatives for detecting and classifying patients who are affected by heart disease. Precise and prompt detection of human heart disease can assist in avoiding heart failure within the initial stages and enhance patient survival. This study proposed a novel Enhanced Multilayer Perceptron (EMLP) framework complemented by data refinement techniques to enhance predictive accuracy. The classification model asses using the CDC cardiac disease dataset and achieved 92% accuracy by surpassing all the traditional methods. The proposed framework demonstrates significant potential for the early detection and prediction of cardiac-related diseases. Experimental results indicate that the Enhanced Multilayer Perceptron (EMLP) model outperformed the other algorithms in terms of accuracy, precision, F1-score, and recall, underscoring its efficacy in cardiac disease detection.