Prediction of Patient Readmission via Machine Learning Algorithms
Samah Alajmani1, Kamal Jambi2

1Samah Alajmani, Faculty of Computing and Information Technology, Computer Science Department, King Abdulaziz University, Jeddah, Saudi Arabia.
2Prof. Kamal Jambi, Professor, Faculty of Computing and Information Technology, Computer Science Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3226-3232 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7770038620/2020©BEIESP | DOI: 10.35940/ijrte.F7770.038620

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Abstract: Predicting the probability of hospital readmission is one of the most vital issues and is considered to be an important research area in the healthcare sector. For curing any of the diseases that might arise, there shall be some essential resources such as medical staff, expertise, beds and rooms. This secures getting excellent medical service. For example, heart failure (HF) or diabetes is a syndrome that could reduce the living quality of patients and has a serious influence on systems of healthcare. The previously mentioned diseases can result in high rate of readmission and hence high rate of costs as well. In this case, algorithms of machine learning are utilized to curb readmissions levels and improve the life quality of patients. Unluckily, a comparatively few numbers of researches in the literature endeavored to address this issue while a large proportion of researches were interested in predicting the probability of detecting diseases. Despite there is a plainly visible shortage on this topic, this paper seeks to spot most of the studies related to predict the probability of hospital readmission by the usage of machine learning techniques such as such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANNs), Linear Discriminant Analysis (LDA), Bayes algorithm, Random Forest (RF), Decision Trees (DTs), AdaBoost and Gradient Boosting (GB). Specifically, we explore the different techniques used in a medical area under the machine learning research field. In addition, we define four features that are used as criteria for an effective comparison among the employed techniques. These features include goal, data size, method, and performance. Furthermore, some recommendations are drawn from the comparison which is related to the selection of the best techniques in the medical field. Based on the outcomes of this research, it was found out that (bagging and DT) is the best technique to predict diabetes, whereas SVM is the best technique when it comes to prediction the breast cancer, and hospital readmission.
Keywords: Hospital Readmission; Machine Learning; Decision Trees; Logistic Regression, Support Vector Machine; Linear Discriminant Analysis; Artificial Neural Networks; Bayes Algorithm; Random Forest; Ada Boost; Gradient Boosting.
Scope of the Article: Machine Learning.