• Rambabu Pemula
• Sahil Tamang
• Mohammed Abdul Muqthadeer
• Somanolla Manohar Reddy

This project presents a robust framework for multi-disease prediction employing advanced machine learning techniques. By integrating diverse medical datasets encompassing patient demographics, symptoms, and medical history, our approach utilizes algorithms such as decision trees and deep learning to construct precise predictive models. We meticulously address data challenges, including missing values and class imbalances, through rigorous preprocessing and feature engineering methods. The iterative model development process involves thorough testing and validation to ensure reliability across various datasets and demographics. Seamless integration with electronic health records facilitates the real-time deployment of predictive models in clinical settings for timely disease prediction and risk assessment. Ultimately, our framework aims to enhance clinical decision-making, optimize patient outcomes, and refine resource allocation strategies through early disease detection. By providing scalable and adaptable solutions for predictive healthcare analytics, this research significantly contributes to proactive and tailored patient care.

Disease prediction, Machine learning, Predictive modeling, Healthcare analytics, Electronic health records, Clinical decision-making, Feature engineering, Resource allocation.

"Multiple Disease Prediction System using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.g466-g476, May-2024, Available :http://www.jetir.org/papers/JETIR2405658.pdf

"Multiple Disease Prediction System using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppg466-g476, May-2024, Available at : http://www.jetir.org/papers/JETIR2405658.pdf