A Gradient Boosting–Based Platform with Fuzzy Linguistic Representation for Cardiovascular Disease Risk Prediction - Amir Saleh; Fadhillah Azmi (Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2026)
Cardiovascular disease (CVD) is one of the most common causes of death around the world. In order to effectively prevent and manage CVD, early detection and prediction of risk are essential. This research introduces a healthcare platform based on CVD risk prediction using advanced machine learning (ML) methods. This platform is designed to provide accurate risk assessment by integrating the gradient boosting (GB) classifier method. Additionally, other ML models are used as comparison algorithms. Initially, this research used preprocessing techniques such as data normalization and data cleaning to tackle outliers in the dataset. Recursive feature elimination (RFE) feature selection approaches are utilized to find features that affect prediction performance, hence lowering the amount of data dimensions and enhancing model performance. Then, using metrics such as accuracy, precision, recall, and F1-score, each model’s performance is evaluated. The modeling results of the suggested approach are then used to create a digital health platform that predicts new input from users. Additionally, fuzzy logic is applied to transform data into linguistic variables to help users find simpler information. Using the proposed GB model and preprocessing method, the platform can make more accurate CVD risk predictions during data validation than other ML methods. When compared to other approaches with lower accuracy, the evaluation results demonstrate that the GB method can achieve the highest prediction accuracy of 94.30%.