Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce

Objectives: To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. Methods: The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the diagnoses of diabetes patients. Improved Density-Based Spatial Clustering of Applications with Noise (I-DBSCAN) is proposed to cluster EHR data to get labels based on the symptoms of patients and map reduction is utilized to process the clustered data in parallel for quick recommendations. Findings: The accuracy of the SVM with the PSO model is 99.20%. The performance of I-DBSCAN is also compared with K-Means and regular DBSCAN using the Silhouette Score, Davies Bouldin Score, and the Calinski Harabasz Score. Also, I-DBSCAN was found to give a more accurate score. Novelty: The extensive volume of diabetes-related information stored in electronic health records (EHRs) through continuous monitoring devices poses a growing difficulty for healthcare professionals to effectively navigate and deliver patient-centered care. Machine Learning techniques like classification and recommendations can be utilized to facilitate early disease diagnosis and recommend appropriate medications.