Analisis Klasifikasi Diagnosa Penyakit Diabetes Melitus Berdasarkan Komparasi Algoritma Supervised Learning - Siridion, Sherly Taurin; Siregar, Bakti (Mutiara: Multidiciplinary Scientifict Journal, 2024)
Diabetes mellitus is a serious health problem that threatens various age groups, including children, teenagers, and adults. In 2021, the mortality rate due to diabetes mellitus reached alarming levels, making it a global threat, especially in Indonesia, where the number of patients reached 19.5 million. Efforts to address diabetes mellitus include early prediction of the disease's risk in patients, and machine learning approaches have shown potential in this regard. This study employs a quantitative method by utilizing secondary data from the UC Irvine Machine Learning Repository titled "Early Stages Diabetes Risk Prediction". The data was obtained from questionnaires filled out by diabetes patients at Sylhet Diabetes Hospital and validated by healthcare professionals. A total of 520 data samples with 17 attributes were used for analysis. The tested Supervised Learning algorithms include Logistic Regression, K-Nearest Neighbour, Support Vector Machine, Random Forest, Naïve Bayes Classifier, Artificial Neural Network, Decision Tree C4.5, and Gradient Boosting Classifier. The research findings reveal that the Random Forest algorithm achieved the highest accuracy of 98.71% in diagnosing diabetes mellitus. This study significantly contributes to enhancing the understanding of diabetes mellitus and has the potential for further development in finding the best algorithm for early disease prediction. It is hoped that this research will make a significant contribution to the efforts in preventing and managing diabetes mellitus, ultimately improving the quality of life for patients and reducing its impact on the population.