We have created a web application that incorporates all of the features, we also designed a user-friendly graphical user interface (GUI) that enables individuals without technical expertise to fully benefit from our work. The web application includes both, Physical Health Assessment (Disease Prediction) and as well as Mental Health Assessment module.
You can visit the Web Application by clicking the link below (Be patient, takes a little to load once)
https://health-assessment.onrender.com/
Implementation of Machine Learning Model to Predict Diseases and Assess Mental Health Disease prediction and mental health assessment are critical components of healthcare that can greatly impact patient outcomes. As a part of this research, our investigation involves an examination of how machine learning methods can be utilized to anticipate illnesses and evaluate mental well-being.
We collected symptom data from patients and used a random forest classifier to predict the disease, achieving an accuracy of 97%. Additionally.
For Mental Health Assessment we asked individuals to answer 53 questions that is based on BSI-53 (Brief Symptom Inventory) and used their responses to predict their mental health condition by using a multiple linear regression model, achieving an R-squared value of 0.99.
Our findings suggest that machine learning models can be effective tools for predicting diseases and assessing mental health conditions.
The high accuracy achieved by the random forest classifier highlights its potential to improve disease diagnosis, while the multiple linear regression model offers a promising approach for assessing mental health conditions. This study provides insights into the development of more accurate and reliable methods for disease prediction and mental health assessment, ultimately improving the quality of care for patients.