Hospital Readmission Prediction using Machine Learning Techniques

Samah Alajmani,Hanan Elazhary

doi:10.14569/ijacsa.2019.0100425

Abstract

One of the most critical problems in healthcare is predicting the likelihood of hospital readmission in case of chronic diseases such as diabetes to be able to allocate necessary resources such as beds, rooms, specialists, and medical staff, for an acceptable quality of service. Unfortunately relatively few research studies in the literature attempted to tackle this problem; the majority of the research studies are concerned with predicting the likelihood of the diseases themselves. Numerous machine learning techniques are suitable for prediction. Nevertheless, there is also shortage in adequate comparative studies that specify the most suitable techniques for the prediction process. Towards this goal, this paper presents a comparative study among five common techniques in the literature for predicting the likelihood of hospital readmission in case of diabetic patients. Those techniques are logistic regression (LR) analysis, multi-layer perceptron (MLP), Naïve Bayesian (NB) classifier, decision tree, and support vector machine (SVM). The comparative study is based on realistic data gathered from a number of hospitals in the United States. The comparative study revealed that SVM showed best performance, while the NB classifier and LR analysis were the worst.

Highlights

Nowadays, numerous chronic diseases, such as diabetes, are widespread in the world; and the number of patients is increasing continuously
Organization of the rest of the paper is as follows: First, we present background about the machine learning techniques considered in this research
An example of an Artificial Neural Network (ANN) is the Multi-Layer Perceptron (MLP), which is typically formed of three layers of neurons and its neurons use nonlinear functions for data processing [16]

Summary

Introduction

Numerous chronic diseases, such as diabetes, are widespread in the world; and the number of patients is increasing continuously. Machine learning, which is one of the most important branches of artificial intelligence, provides methods and techniques for learning from experience [3] Researchers often use it for complex statistical analysis tasks [4]. It is a wide multidisciplinary domain which is based on numerous disciplines including, but not limited to, data processing, statistics, algebra, knowledge analytics, information theory, control theory, biology, statistics, cognitive science, philosophy, and complexity of computations. This field plays an important role in term of discovering valuable knowledge from databases which could contain records of supply maintenance, medical records, financial transactions, applications of loans, etc. An example of an ANN is the Multi-Layer Perceptron (MLP), which is typically formed of three layers of neurons (input layer, output layer, and hidden layer) and its neurons use nonlinear functions for data processing [16]

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Results

Conclusion