Predictive Analysis for Multiple Disease Identification using Machine Learning

The availability of diverse health data, including medical records, genetic information, and lifestyle details, presents an opportunity to gain a comprehensive understanding of individual health profiles and disease risks. However, effectively integrating these disparate data sources remains a significant challenge. This study proposes a novel approach to address this challenge by combining deep learning techniques and ensemble methods. The proposed model is capable of simultaneously predicting an individual's risks for a range of major chronic and acute diseases, including cardiovascular disease, cancers, diabetes, and more. By integrating advanced machine learning techniques, the model can effectively integrate structured and unstructured data, capturing the complex relationships between different data types and disease outcomes. This approach enables early disease detection and facilitates timely intervention, leading to improved patient outcomes. The study highlights the importance of combining diverse data sources and employing machine learning techniques to develop accurate and comprehensive predictive models for multi-disease risk assessment. This research contributes to the advancement of personalized healthcare and preventive medicine.