A Deep Learning Model for Decision Making in Healthcare Systems

Abstract

Decision Making (DM) is one of the domains that provide decisions based on improvement in business without the involvement of humans. This is mainly focused on giving suggestions to various companies to increase their online product, disease prediction, and also in many applications. Decision-making is used in many research applications such as the medical domain, online marketing, E-commerce, Filtering of Email, and other types of domains that help experts to get better decisions. It is identified that machine learning (ML) algorithms have several disadvantages in finding accurate decisions on various applications. Huge research is done on ML algorithms to find accurate patterns in decision-making algorithms to take automated decisions. ML algorithms are very weak in the processing of large datasets and also in statistical analysis. In this domain, deep learning (DL) plays a significant role in finding accurate patterns in detecting decision-making in multiple domains. In this paper, a deep learning model is introduced to find the disease patterns from the medical data to take the correct decisions and also to predict the risk based on the status of the disease. The proposed deep learning model is the combination of several models. Thus the decision-making is applied to know the status of the disease. Experiments are conducted on Heart-2 Dataset, Haberman’s Survival dataset, and Pima Indians Diabetes datasets and show the comparative performance.