Diabetes Detection Based on Health Conditions Using Advanced Learning Algorithm

Diabetes is a common long-term metabolic illness that impacts millions of people globally. Diabetes must be identified and treated early to avoid significant health issues. In recent years, the application of advanced learning algorithms in healthcare has shown promising results in automating disease detection and diagnosis. This study focuses on developing a diabetes detection model utilizing ensemble learning (EL) techniques and health condition data. The proposed model utilized diverse health-related features, including but not limited to age, body mass index (BMI), blood pressure, glucose levels, family history, and lifestyle factors. A comprehensive dataset containing these variables for a large cohort of individuals, both diabetic and non-diabetic, is used for training and evaluation. The proposed approach combines Gradient Boosting Machines (GBM) with transfer learning (TF) to create a robust and accurate diabetes detection model. The trained model k-Nearest Neighbors (k-NN) is used to analyze the complex relationships between the input features and the presence of diabetes, thereby learning to identify patterns that may indicate the disease with the help of transfer learning. The feature extraction technique Logarithmic transformation is used to extract the interesting patterns. Ultimately, the proposed model's performance is evaluated using a range of assessment metrics, such as F1-score, accuracy, precision, specificity, and recall. The results obtained from the evaluation demonstrate the model's ability to distinguish between individuals with diabetes and those without effectively. This research contributes to the growing body of knowledge on integrating advanced learning algorithms in healthcare applications, specifically in diabetes detection. The potential impact of this study includes the early identification of individuals at risk of developing diabetes, leading to timely interventions and personalized healthcare strategies. Additionally, the methodology and insights from this work can be extended to other disease detection and prediction tasks in medical research.