Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

Abstract

With the worldwide analysis, heart disease is considered a significant threat and extensively increases the mortality rate. Thus, the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System (CDSS). Generally, CDSS is used to predict the individuals' heart disease and periodically update the condition of the patients. This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers. Here, the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease. Then, the optimization is achieved using the Adam Optimizer (AO) model with the publicly available dataset known as the Stalog dataset. This flow is used to construct the model, and the evaluation is done with various prevailing approaches like Decision tree, Random Forest, Logistic Regression, Naive Bayes and so on. The statistical analysis is done with the Wilcoxon rank-sum method for extracting the p-value of the model. The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy. This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage. Thus, the earlier treatment process helps to eliminate the death rate. Here, simulation is done with MATLAB 2016b, and metrics like accuracy, precision-recall, F-measure, p-value, ROC are analyzed to show the significance of the model.

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

“…Time series forecasting reveals future trends through the historical data of the time series, i.e., the seasonal cycle can be obtained through time series forecasting thus facilitating the development of the length of time for which the ordering strategy applies similarly to the method which Benvenuto et al used [7]. The outliers in this prediction are clustered and denoised to obtain a data series that is easy to deal with [8]. After data processing and cleaning, a general observation of the data shows that the series contains both a longterm trend and a seasonal effect with a 24-week cycle, and the sum of seasonal factor is approximately zero, so the seasonal model could apply to the forecast.…”