Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in

[摘要] Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globestart studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from badconsequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilizedeffectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizesof data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets forearly diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggestedMachine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification,33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. Inthe original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are usedfor the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which iscollected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A.Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHTalgorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67%accuracy for early estimation of diagnosis of heart disease.

[学科分类] 计算机科学（综合）

[关键词] Internet of medical things;machine learning;medical data;random forest;internet of things;diagnosis;AHT