| Autor: |
Bhat, Salliah Shafi, Banu, Madhina, Ansari, Gufran Ahmad, Selvam, Venkatesan |
| Zdroj: |
International Journal of Electronic Healthcare; 2023, Vol. 13 Issue: 3 p231-246, 16p |
| Abstrakt: |
Diabetes is a major severe disease that affects a lot of people worldwide. Technical advances have rapid impact on many aspects of human life whether it is healthcare profession or any other field. The disorder has an impact on society. Machine learning algorithms (MLA) can aid in predicting the chance of developing diabetes at a young age, and assist in improving diabetes clinical condition. The proposed framework can be used in the healthcare industry for diabetes detection and prediction in North Kashmir. Four MLA have been successfully used in the experimental study, random forest, K-nearest neighbour, support vector machine and naive Bayes, respectively. KNN is the most accurate classifier, with the highest accuracy rate of 97.29% in comparison to the other methods with the balanced dataset. Overall, this study enables us to effectively identify the prevalence and prediction of diabetes. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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