期刊
JOURNAL OF SUPERCOMPUTING
卷 77, 期 5, 页码 5198-5219出版社
SPRINGER
DOI: 10.1007/s11227-020-03481-x
关键词
Artificial intelligence; Diabetes; Data mining techniques; Naï ve Bayes classification; Random forest classification
类别
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2018R1D1A1B07043302]
- National Research Foundation of Korea [2018R1D1A1B07043302] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Healthcare practices involve collecting patient data to aid in accurate diagnosis by doctors, with artificial intelligence utilized for disease classification techniques to improve diagnostic accuracy.
Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are only utilized for analysis by a doctor who then ascertains the disease using his/her personal medical expertise. The artificial intelligence has been used with Naive Bayes classification and random forest classification algorithm to classify many disease datasets like diabetes, heart disease, and cancer to check whether the patient is affected by that disease or not. A performance analysis of the disease data for both algorithms is calculated and compared. The results of the simulations show the effectiveness of the classification techniques on a dataset, as well as the nature and complexity of the dataset used.
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