Early Detection of Diabetes Mellitus in Women via Machine Learning

2024-12-28T13:00:41+07:00

Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as secondary data, such as pregnancy-related metrics, from the UCI Pima Indians Diabetes Database, which contains 768 records with eight attributes. The study evaluates the performance of three machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest, using metrics such as accuracy, precision, recall, and F1-score. Among these models, the Decision Tree demonstrates excellent performance for Class 0, with precision, recall, and F1-score all at 0.97. However, its performance for Class 1, while decent, leaves room for improvement, achieving a precision of 0.80 and a recall of 0.84, resulting in an F1-score of 0.82. Logistic Regression also performs well for Class 0, with a precision of 0.95 and a recall of 0.99, yielding an F1-score of 0.97. Yet, it struggles with Class 1, where its precision is high at 0.93, but its recall drops significantly to 0.68, producing an F1-score of 0.79. Lastly, Random Forest emerges as the best-performing model overall, achieving an accuracy of 0.96. It excels for Class 0, with a precision of 0.96 and a recall of 0.99, leading to an F1-score of 0.97. For Class 1, it maintains high precision (0.93) but exhibits moderate recall (0.74), resulting in an F1-score of 0.82.

Advancing Cardiovascular Risk Prediction: A Review of Machine Learning Models and Their Clinical Potential

2024-12-31T06:06:31+07:00

This study conducts a systematic literature review on the application of machine learning technology in predicting heart disease risk. A total of 20 recent articles were identified and analyzed to evaluate the most used algorithms and their performance. The results show that Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) are the most frequently applied models, with average accuracies of 89.56%, 83.14%, 83.14%, 82.57%, and 79.40%, respectively. In addition to comparing accuracy, this review also evaluates the strengths, weaknesses, and potential challenges of implementing each algorithm in clinical applications. The analysis reveals that Random Forest demonstrates high stability and accuracy, making it the leading candidate for large-scale clinical heart disease risk prediction applications. These findings are expected to provide new insights for the development of more accurate, reliable, and clinically deployable machine learning predictive models to support medical decision-making.

Journal of Electrical Technology UMY

Early Detection of Diabetes Mellitus in Women via Machine Learning

Advancing Cardiovascular Risk Prediction: A Review of Machine Learning Models and Their Clinical Potential