Diabetes Prediction using SVM

The rising levels of chronic diseases like diabetes have posed a big challenge to the health care systems of the world, and it is necessary to prevent them and provide effective management measures. This study explores the use of predictive analytics based on Artificial Intelligence (AI) to predict people with a risk of developing diabetes using medical and demographic information. Several supervised learning algorithms, such as Logistic Regression, Random Forest are applied and compared in terms of such performance measures as accuracy, precision, recall, and F1-score. Techniques of data preprocessing such as management of missing values, coding of categorical variables and normalization are used to optimize model performance. Though the dataset is not based on real-time wearable devices, it includes key physiological measurements that are usually measured using wearable and remote patient monitoring systems. Thus, it is possible to extrapolate the present study findings to the real-time health care settings where a stream of data is accessible. The findings show that predictive models using AI can easily be used to detect high-risk persons to initiate early interventions and tailored treatment plans. The study points to the opportunity of AI and healthcare data combination to enhance the management and prediction of diseases. This study offers a starting point to any future research that may include real-time wearable information to maintain a constant flow of monitoring and proactive healthcare provision.