Multi-Disease Prediction based on Symptoms using DL

Abstract

The primary objective of this research is to develop an accurate and efficient model to predict various diseases by analyzing patients' symptoms, thereby enabling early detection, intervention, and personalized treatment plans. Our proposed system employs advanced feature extraction techniques and state-of-the-art Deep Learning (DL) algorithms to analyze and classify symptom patterns, ultimately predicting the most likely diseases associated with the given symptoms. We utilized a comprehensive dataset containing symptom data for numerous diseases, and our system was trained using DL techniques. The performance of the proposed model was evaluated through multiple performance parameters, including accuracy, sensitivity, and specificity. Our experimental results demonstrate the effectiveness and potential of the proposed system in predicting multiple diseases based on symptoms with high accuracy. This study highlights the potential of DL in revolutionizing the field of medical diagnosis and personalized medicine, ultimately improving patient outcomes and healthcare efficiency. The integration of DL with healthcare can bring about a revolution in personalized medicine, and the accurate prediction of diseases can enable early intervention and improve patient outcomes. The article highlights the potential of DL methods in disease prediction and emphasizes the need for further research to overcome the current limitations and challenges. Overall, the article serves as a guide for researchers and healthcare professionals in understanding the role of DL in disease prediction and its implications for the future of healthcare.