Heart disease prediction using hyper parameter optimization (HPO) tuning

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{VALARMATHI:2021:BSPC,

• author = "R. Valarmathi and T. Sheela",
• title = "Heart disease prediction using hyper parameter optimization ({HPO)} tuning",
• journal = "Biomedical Signal Processing and Control",
• volume = "70",
• pages = "103033",
• year = "2021",
• ISSN = "1746-8094",
• DOI = "doi:10.1016/j.bspc.2021.103033",
• URL = "https://www.sciencedirect.com/science/article/pii/S1746809421006303",
• keywords = "genetic algorithms, genetic programming, Hyper parameter tuning, Heart disease, Grid search, Randomized search, TPOT classifier",
• abstract = "Coronary artery disease prediction is considered to be one of the most challenging tasks in the health care industry. In our research, we propose a prediction system to detect the heart disease. Three Hyper Parameter Optimization (HPO) techniques Grid Search, Randomized Search and Genetic programming (TPOT Classifier) were proposed to optimize the performance of Random forest classifier and XG Boost classifier model. The performance of the two models Random Forest and XG Boost were compared with the existing studies. The performance of the models is evaluated with the publicly available datasets Cleveland Heart disease Dataset (CHD) and Z-Alizadeh Sani dataset. Random Forest along with TPOT Classifier achieved the highest accuracy of 97.52percentfor CHD Dataset. Random Forest with Randomized Search achieved the highest accuracy of 80.2percent, 73.6percent and 76.9percent for the diagnosis of the stenos is of three vessels LAD, LCX and RCA respectively with Z-Alizadeh Sani Dataset. The results were compared with the existing studies focusing on prediction of heart disease that were found to outperform their results significantly",

}

Genetic Programming entries for R Valarmathi T Sheela

Citations