A hybrid machine learning method for prediction of heart disease

Anxhela Gjecka; Senada Bushati

doi:10.1117/12.3012228

19 December 2023 A hybrid machine learning method for prediction of heart disease

Anxhela Gjecka, Senada Bushati

Proceedings Volume 12936, International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023); 1293607 (2023) https://doi.org/10.1117/12.3012228
Event: International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023), 2023, Istanbul, Turkey

Abstract

One of the biggest causes of mortality globally is heart disease (HD). The Support Vector Machine (SVM) method was employed in this work to forecast heart disease cases using data on heart disease. The chosen target group served as the basis for categorization. The primary goal of this forecast is to offer an accurate diagnosis and sufficient lead time to avoid a tragic outcome. Initially, this classification was constructed using a hybrid method, that is, the use of SVM and KNN techniques. Hybrid methods provide a much more accurate prediction than simple methods. The results from the study were satisfactory, and the experimental findings showed how well the system worked for precise predictions. The area under the curve score was 0.86. The proposed system easily identifies and classifies healthy versus unhealthy individuals. The execution time of the algorithm is acceptable, and at the same time, increasing the performance using classification reduces, on average, the execution time and memory usage by 98.75%. The use of SVM and kNN can help doctors diagnose sick patients promptly with satisfactory efficacy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Anxhela Gjecka and Senada Bushati "A hybrid machine learning method for prediction of heart disease", Proc. SPIE 12936, International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023), 1293607 (19 December 2023); https://doi.org/10.1117/12.3012228

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