Diabetes Prediction Using Machine Learning

The research analyzes machine learning methods for predicting diabetes through Pima Indians Diabetes Dataset analysis. The optimization of XGBoost and Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) through Optuna resulted in tests on clinical features including glucose, BMI and insulin. The predictive performance of XGBoost and LR reached 82.03% accuracy and 88.24% precision due to their strong ability to detect positive cases. All four prediction models demonstrated insufficient recall performance at 41.67% which resulted in missing 58% of diabetic cases thus making them unsuitable for clinical practice. The F1-scores indicate that medical diagnostic precision and recall remain difficult to balance because XGBoost achieved 56.60% and SVM reached 54.55% and RF obtained 50.91% (XGBoost: 56.60%, SVM: 54.55%, RF: 50.91%). The clinical adoption of XGBoost ensemble methods for diabetes screening needs improved sensitivity and dataset diversity to reach practical application standards. Diabetes mellitus has developed into a major worldwide health challenge because it affects people from diverse age groups and economic levels. The condition features prolonged high blood glucose levels, which occur because the body either produces insufficient insulin or cannot efficiently use the insulin it makes. The World Health Organization (WHO) identifies diabetes as a major global health problem since the number of cases continues to increase rapidly, while being ranked as a top death-causing condition worldwide. The World Health Organization WHO, 2021) reported that diabetes affected 422 million people worldwide in 2014 while forecasting this number to grow unless effective preventive steps are adopted (WHO, 2021). The increasing number of diabetes cases demonstrates the necessity for superior diagnostic methods and early treatment approaches, and enhanced disease management to minimize long-term health complications.The medical community divides diabetes into two main subtypes, which are Type 1 and Type 2. Type 1 diabetes occurs when the immune system attacks pancreatic beta cells, which produce insulin, resulting in an absolute lack of this hormone. Children and adolescents commonly develop this type of diabetes, which needs continuous insulin treatment for survival. The majority of diabetes diagnoses worldwide belong to Type 2 diabetes, which develops because patients have both insulin resistance and insufficient insulin production. Type 2 diabetes differs from Type 1 because its origin stems from adjustable lifestyle elements like poor eating habits and lack of physical activity, together with obesity. The development of diabetes can be attributed to genetic susceptibility as well as age and specific medical conditions, including polycystic ovary syndrome (PCOS) (International Diabetes Federation, 2021).The uncontrolled nature of diabetes produces severe health complications, which become a major concern for patients. The persistent elevation of blood sugar levels creates severe health risks that produce fatal medical conditions such as cardiovascular disease and stroke and kidney failure and nerve damage (neuropathy), and vision impairment (retinopathy). Lower-limb amputations arise primarily from diabetes, while the illness simultaneously leads to substantial deterioration in patients' lifestyle quality. Early diagnosis remains essential because it enables patients to obtain prompt access to proper treatment methods combined with necessary lifestyle changes. Diagnosis of diabetes depends on testing fasting blood glucose levels and conducting oral glucose tolerance tests and measuring glycated hemoglobin (HbA1c). The diagnostic tools demonstrate effectiveness, but they possess multiple restrictions. Medical facilities with laboratories are necessary for blood tests as these procedures take longer to complete, and limited accessibility exists for people living in distant areas with low household income. People with diabetes might go without a diagnosis because their condition shows no symptoms when it first develops.Machine learning-based diagnostic tools act as affordable, convenient solutions for diabetes screening in locations that lack sufficient healthcare facilities. Predictive models generate important health risk predictions for individuals which allow them to take preventive measures that stop diabetes development. The worldwide increase in diabetes cases requires innovative data-based solutions to help identify potential problems and stop their development. The research develops a machine learning model for diabetes prediction through analysis of authentic patient clinical data to advance knowledge in this field. The research aims to develop improved detection methods that will strengthen diabetes management practices, along with advancing technological healthcare solutions for medical practice.