Citation

Joshi, Neeraja and Dave, Tejal (2025) Improved Accuracy for Heart Disease Diagnosis Using Machine Learning Techniques. Journal of Informatics and Web Engineering, 4 (1). pp. 42-52. ISSN 2821-370X

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Abstract

Accurate diagnosis of cardiovascular diseases (CVDs) is vital as people face many health issues due to CVD. Worldwide, more than 17 million people lose their lives each year due to CVD. This work primarily focuses on diagnosing heartdisease before an explicit visit to the expert doctor. Machine learning-based systems have been found helpful in all applications, including medical ones, as they can learn human-like expert knowledge and utilize it subsequently. This work performs the classification of heart disease utilizing the subject's vital parameters. Ordinary people and patients need help understanding pathological laboratory results available after Testing and have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression to various machine learning-based systems,including back propagation neural network, support vector machine(SVM), and k-nearest neighbor, are developed for heart disease classification. The proposed system (i) takes 13 vital parameters, including age, sex, chest pain type, fasting blood sugar,resting ECG, etc., as available from the Cleveland database, (ii) processes them with tuned machine learning systems, and (iii) transforms sensor inputs to stroke stage classification. To ascertain the proposed system's efficacy, all methods' performances are compared with similar work performed on the same standard-Cleveland database. Simulation results show 100 percent correct diagnosis and the robustness of SVM-based approaches for test data

Item Type:	Article
Uncontrolled Keywords:	Neural network, normalization,classification, support vector machine (SVM)
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	25 Jun 2025 03:55
Last Modified:	25 Jun 2025 03:55
URII:	http://shdl.mmu.edu.my/id/eprint/13982

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