AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes
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...
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| Published in | The Journal of supercomputing Vol. 77; no. 5; pp. 5198 - 5219 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. |
| Author | Vimal, S. Kaliappan, M. Jackins, V. Lee, Mi Young |
| Author_xml | – sequence: 1 givenname: V. surname: Jackins fullname: Jackins, V. organization: Department of IT, National Engineering College – sequence: 2 givenname: S. surname: Vimal fullname: Vimal, S. organization: Department of IT, National Engineering College – sequence: 3 givenname: M. surname: Kaliappan fullname: Kaliappan, M. organization: Department of Computer Science and Engineering, Ramco Institute of Technology – sequence: 4 givenname: Mi Young orcidid: 0000-0002-8139-7091 surname: Lee fullname: Lee, Mi Young email: miylee@sejong.ac.kr organization: Department of Software, Sejong University |
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| Title | AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes |
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