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Heart Patient Triage Prediction of Clinical Outcomes Using Machine Learning Models

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DOI: 10.23977/fbb2020.012


Baohua Jin, Qinghua He, Huaiguang Wu, Ming Cheng and Pengjie Xie

Corresponding Author

Baohua Jin


In the medical field, the analysis of different clinical and pathological data by medical experts is a complicated process. Therefore, it is important to build a framework that can instantly and effectively identify the prevalence of heart disease in thousands of samples. Hence, the paper proposed a new method for diagnosing heart disease. The proposed machine learning method is based on a hybrid optimization algorithm of random search and grid search of a random forest and a parameter optimization algorithm of support vector machine grid search. The model, in predicting the presence or absence of heart disease in patients, has a maximum accuracy of 89%, which not only aided doctors in accurately predicting and diagnosing various diseases, but also helps patients with early diagnosis.


Aided diagnostic, random search and grid search of a random forest algorithm, support vector machine grid search algorithm, Machine learning

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