Performance Optimization of Support Vector Machine with Adversarial Grasshopper Optimization for Heart Disease Diagnosis and Feature Selection

• Published in: International journal of advanced computer science & applications 2024-01, Vol.15 (8)
• Main Authors: Tang, Nan, Wang, Lele, Li, Kangming, Liu, Zhen, Dai, Yanan, Hao, Ji, Zhang, Qingdui, Sun, Huamei, Qi, Chunmei
• Format: Article
• Language: English
• Subjects: Algorithms; Cardiovascular disease; Developing countries; Diagnosis; Disorders; Feature selection; Grasshoppers; Heart; Heart diseases; LDCs; Medical diagnosis; Optimization; Optimization techniques; Support vector machines
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150813

The World Health Organization reports that cardiac disorders result in approximately 1.02 million deaths. Over the last years, heart disorders, also known as cardiovascular diseases, have significantly influenced the medical sector due to their immense global impact and high level of danger. Unfortunately, accurate prognosis of heart problems or CD, as well as continuous monitoring of the patient for 24 hours, is unattainable due to the extensive expertise and time required. The management and identification of cardiac disease pose significant challenges, particularly in impoverished or developing nations. Moreover, the absence of adequate medical attention or prompt disease management can result in the individual's demise. This study presents a novel optimization technique for diagnosing cardiac illness utilizing Support Vector Machine (SVM) and Grasshopper Optimization Algorithm (GOA). The primary objective of this approach is to identify the most impactful characteristics and enhance the efficiency of the SVM model. The GOA algorithm, which draws inspiration from the natural movements of grasshoppers, enhances the search for features in the data and effectively reduces the feature set while maintaining prediction accuracy. The initial stage involved pre-processing the ECG data, followed by its classification using several algorithms such as SVM and GOA. The findings demonstrated that the suggested approach has markedly enhanced the effectiveness and precision of heart disease diagnosis through meticulous feature selection and model optimization. This approach can serve as an efficient tool for early detection of heart disease by simplifying the process and enhancing its speed.