Disease Prediction with Ayurvedic Remedies Suggestion and Hospital Recommendation using ML and DL

Leveraging machine learning (ML) and deep learning (DL) for disease prediction has demonstrated tremendous potential in improving diagnostic efficiency and accuracy in the modern healthcare landscape. Diagnosis of disease at early stage saves the patient from several critical health issues. Many diseases can be diagnosed based on the symptoms. This paper we implemented model which can predict disease based on provided symptoms and suggest the Ayurvedic remedies based on predicted disease. With this we have implemented hospital recommendation based on city and state. Random Forest and Decision tree algorithms from machine learning and CNN from deep learning are used for prediction of disease and KNN algorithm used for nearby hospital recommendation. Freely available and open access datasets are used for implementation. Dataset consist of 4920 records, 41 unique disease and 132 unique symptoms. Accuracy measures are used to assess the models Performance; the Accuracy of decision tree and random forest is ${9 5 \%}$ and ${9 8 \%}$, deep learning algorithm performed better on both train and test data with accuracy of ${9 9 \%}$. This combined technique really helpful to improve patient outcomes with Ayurvedic remedies suggestion and nearby hospital recommendations.