An Efficient Ensemble Model for Various Scale Medical Data

Abstract

Electronic Health Records (EHRs) are the digital form of patients’ medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient with different datasets. The MDRL gives the highest accuracy value. It saves the processing time instead of processing four different algorithms sequentially; it executes the four algorithms in parallel. We implement five different algorithms on five variant datasets which are Heart Disease, Health General, Diabetes, Heart Attack, and Covid-19 Datasets. The four algorithms are Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multi-layer Perceptron (MLP). In addition to MDRL (our proposed ensemble model) which includes MLP, DT, RF, and LR together. From our experiments, we conclude that our ensemble model has the best accuracy value for most datasets. We reach that the combination of the Correlation Feature Selection (CFS) algorithm and our ensemble model is the best for giving the highest accuracy value. The accuracy values for our ensemble model based on CFS are 98.86, 97.96, 100, 99.33, and 99.37 for heart disease, health general, Covid-19, heart attack, and diabetes datasets respectively.