TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
The Investigation of Employing Supervised Machine Learning Models to Predict Type 2 Diabetes Among Adults |
| 영문제목(English Title) |
The Investigation of Employing Supervised Machine Learning Models to Predict Type 2 Diabetes Among Adults |
| 저자(Author) |
Tareq Alhmiedat
Mohammed Alotaibi
|
| 원문수록처(Citation) |
VOL 16 NO. 09 PP. 2904 ~ 2926 (2022. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%. |
| 키워드(Keyword) |
machine learning
medical diagnosis
ype 2 diabetes
diabetic prediction
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| 파일첨부 |
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