Analysis and Comparison of Prediction of Heart Disease Using Novel Genetic Algorithm and XGBoost Algorithm

G. Pavithraa¹, Sivaprasad¹

¹ Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu, India

Abstract

Aim : Prediction of coronary sickness utilizing novel genetic algorithm and contrasting its accuracy with XG boost algorithm. Materials and methods : Two models are proposed for foreseeing the accuracy (%) of coronary infection. To be unequivocal, a novel genetic algorithm and XG boost algorithm. Here we take 20 samples each for evaluation and analysis. Result : The novel genetic algorithm gives better accuracy (88.35%) than the XG boost accuracy (81.88%). Along these lines the genuine meaning of novel genetic algorithms is superior to XGBoost calculation with significance value of 0.115 Conclusion : From the outcome, it may very well be accumulated that a novel genetic algorithm helps in expecting the coronary affliction with more precision shown distinctively corresponding to XGBoost algorithm.

Imprint

G. Pavithraa, Sivaprasad. Analysis and Comparison of Prediction of Heart Disease Using Novel Genetic Algorithm and XGBoost Algorithm. Cardiometry; Issue 25; December 2022; p.778-782; DOI: 10.18137/cardiometry.2022.25.778782; Available from: https://www.cardiometry.net/issues/no25-december-2022/novel-genetic-algorithm

Keywords

Heart Disease, Novel Genetic Algorithm, XGBoost, Machine Learning, Coronary sickness, Accuracy, Samples

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