A Machine Learning Approach for Predictive-Probability Analysis and Preventive Strategies in Dengue Feve

Abstract

Dengue fever, a common mosquito-borne viral infection, often goes undiagnosed until severe symptoms appear. Early detection is important for effective management and prevention of complications. This study explores the potential of machine learning (ML) and artificial intelligence (AI) to develop predictive models for dengue fever and severity assessment. This study used secondary datasets including patient data including medical history, environmental factors, symptoms and possible genetic predisposition to train various machine learning algorithms such as CNN, Linear Regression, Random Forest and K-Nearest Neighbors (KNN). This method involves data collection, preprocessing, feature selection, model training, cross-validation, and hyperparameter tuning to optimize performance and avoid overfitting. Models are evaluated based on metrics such as accuracy and AUC-ROC, including attribute importance scores and shape quality analysis. The dataset consisted of 531 patient records with 20 attributes each, which ensured data quality through preprocessing steps such as handling missing values and encoding categorical variables. A high-performance computer, cloud resources, powerful hardware and software including Python, TensorFlow, PyTorch and Skit-Learn are essential for implementation. Effective project management and financial analysis ensure research success and sustainability. Studies have shown that linear regression and random forest models achieve the highest accuracy (99%), followed by CNN (97%) and KNN (54%). These findings underscore the potential of machine learning to enhance dengue prediction, improve patient outcomes, and inform public health strategies.