Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Abstract

Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

“…Multi-tier weighted ensemble learning (MTWEL) is developed that optimizes the parameters of all the learning algorithms used as the ensemble using genetic algorithm (GA). Heart disease is predicted using the algorithm and good performance is observed [22]. Feature importance selection is applied on the features selected for prediction.…”