Design and deployment of e-Health system in perspective of developing countries: machine learning based approach
SM, Mohamad AM, Zishan SR, Ahasan R & Sharun . 2020
In this research, for the disease identification part, machine learning techniques have been applied to identify three diseases which are Dengue, Diabetes, and Thyroid. Decision Tree, Gaussian Naive-Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Multilayer Perceptron, and Support Vector Machine Classifiers have been used for all three diseases. The E-health system comprised of disease identification model, medical specialist recommendation model, and the medicine suggestion model has been deployed on the web. The medical specialist recommendation model and the medicine suggestion model results are based on the finding of the disease identification model. Any user can insert their disease-specific data to use these three features of the E-health system. For the disease identification model, Multilayer Perceptron for Dengue, Logistic Regression for Diabetes, and Random Forest for Thyroid performed the best with accuracies of 88.3%, 82.5%, and 98.5% respectively. These classifiers also showed good precision, recall, and F1 score.
In this research, for the disease identification part, machine learning techniques have been applied to identify three diseases which are Dengue, Diabetes, and Thyroid.