Hybrid Machine Learning for Disease Diagnosis: A Review of Case Studies and Performance Evaluation Using Multi-Source Data

Abstract

The review paper discusses the development and assessment of hybrid machine learning frameworks for early disease diagnosis using multi-source clinical data. It highlights the importance of early disease forecasting in healthcare, as it allows for more efficient illness management, mitigating symptom severity, and decelerating disease development. Traditional disease prediction methods often face challenges, such as reliance on isolated data sources and the existence of imbalanced datasets. Hybrid machine learning models offer a robust approach to address these shortcomings by integrating the advantages of multiple machine learning methods to enhance predictive accuracy and resilience. Case studies have demonstrated the effectiveness of hybrid machine learning models in predicting coronary heart issues, Alzheimer's brain related issues, diabetes, and lung cancer. This review study discusses various studies on the use of deep learning models in medical image analysis, heart disease prediction, and brain tumor detection. The study discusses various methods, such as hybrid machine learning, ensemble machine learning, multi-source transfer learning, convolutional neural networks, and cloud-enabled access control models. It also discusses the role of advanced learning models such as AI and deep learning in early detection of chronic diseases and the spread infection-based diseases. The study also discusses the challenges and opportunities in using machine learning in cardiovascular disease prediction, with some focusing on the use of jellyfish optimization algorithm and others on the use of boosting techniques. The review concludes by focus attention on the importance of understanding the role of machine learning in healthcare and the potential for future advancements in this field.