Disease prediction using machine learning and image processing

Abstract

Current machine learning models for healthcare analysis focus on one disease per analysis, such as diabetes, liver, malaria, pneumonia diseases. Our project aims to predict multiple diseases using machine learning algorithms, streamlit, Flask API, and Python pickling to save and load the model's behavior. By analyzing all parameters that cause diseases, our system can detect the maximum effects a disease will cause. A study using a large medical image dataset trained a deep learning model to predict diseases using transfer learning techniques. The proposed approach achieved high accuracy, outperforming other traditional methods, and has potential applications in clinical settings to reduce human error, improve diagnostic accuracy, and reduce the time required for diagnosis. This project can help people by monitoring their condition and taking necessary precautions to increase life expectancy. Overall, this study demonstrates the effectiveness of using machine learning and image processing for disease prediction and provides valuable insights into future research in this area.