Ischemic Heart Disease Prediction Using Optimized Squirrel Search Feature Selection Algorithm

Abstract

In recent years, the volume in globally recognized medical data sets are increasing both with attributes and number of records. Machine learning algorithms aiming to detect and diagnose ischemic heart diseases requires high efficacy and judgment. The state of art Ischemic heart disease data sets presents several issues, including feature selection, sample size, sample imbalance, and lack of magnitude for some characteristics etc. The proposed study is primarily concerned with improving feature selection and reducing the number of features yet giving better decisions. In this study, to pick salient aspects of heart illness, an improved squirrel search optimization algorithm with a meta-heuristic approach is proposed. Comparison study of the proposed ischemic heart disease squirrel search optimization (IHDSSO) model in conjunction with random forest classifier ensures better feature selection and accuracy over 98% with respect to other state-of-the-art optimization algorithms.

Using the Quine McCluskey Binary Classifier (QMBC) for Heart Disease Prediction

“…It has promising potential for healthcare applications, but there's room for improvement in convergence accuracy and speed. [5] 1) Advantages  High accuracy: Achieved 100% and 99.76% accuracy for specific datasets.…”

Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases

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Hybrid model of convolutional neural network and long short term memory for heart disease prediction

“…The field of predicting heart disease makes the implementation of machine learning. There are significant possibilities for enhancing treatment results through the analysis of data, which can help identify patterns and create prognostic systems for personalized assessment, forecast, and therapy [4]. Machine learning holds significant promise in advancing medical solutions across the entire spectrum, spanning from initial discovery and prediction to informed decision-making.…”

“…Figure 1. Heart disease prediction framework [4] The initial stage is to gather public databases for heart disease diagnosis that have been provided by various healthcare organizations. The next stage of data processing is transforming unprocessed data into useful patterns [6].…”