Optimized Doctor Recommendation System using Supervised Machine Learning

In the past decade, we have seen many patients and healthcare problems. Due to this, patients find difficulty choosing doctors according to their disease. Several Machine Learning (ML) based techniques already exist to predict doctors based on patient's health conditions. However, it is essential to accurately recommend doctors to patients with low errors based on patients' health conditions. Therefore, we propose a method that assigns quantitative importance (weight) to each feature using an ML technique. Moreover, we offer a framework to recommend doctors based on the similarity score and doctor's skill score, which utilizes weight prediction to enhance operational efficiency. Additionally, on real-world datasets, the effectiveness of the proposed framework is demonstrated empirically by lowering the average loss by roughly 34% and 3% as compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), respectively. The outcome demonstrates that the algorithm can efficiently recommend doctors to patients compared to state-of-the-art techniques. This analysis technique aid patients in opting for the right doctor.