Abstract
Hospitalization costs accrue a huge burden on the economy; thus, we need a hospital readmission system for predicting treatment costs associated with the patient admitted at the hospital. A novel prediction model for readmissions of patients suffering from disabilities and for patients with comorbidities that pose critical health risks thereby escalating healthcare costs and posing a threat on the survival of patients is highly recommended for patients at high risk of readmission to be proactive during treatment thereby reducing readmission cost. As per data of the National Health Protection Mission hospitalized between 2016 and 2022 in India, more than 9000 patients were readmitted and took treatment after a significant lapse. This chapter proposed a machine learning framework with all key elements of patients resulting discrimination ability and predicting financial analysis to estimate targeted patients thereby identifying risk factors, and a model was tested on an Indian government repository of healthcare dataset and achieved 97.9% correct prediction readmission in hospitals.
TopLiterature Review
An intelligent healthcare system discovers trends in readmission data of various departments, thereby predicting and assessing readmission rates. Organizations utilize multiple methods to examine their data to predict future events. An intelligent healthcare system is a combination of statistical analysis and various data mining techniques, such as association, classification, clustering and pattern matching. It comprises the exploration and preparation of data, defining an intelligent system, and follows its process. In the development of an intelligent healthcare system, various authors have carried out research primarily on prescriptive analytics using descriptive analytics, diagnostic analytics and predictive analytics and have proposed a framework for the healthcare industry of real-time patients using support vector machines. Good accuracy was achieved, but biased datasets resulted in false predictions (Babar et al., 2016; Bossen & Piras, 2020; Jin et al., 2016). For dealing with ambiguous, biased datasets, a framework for COVID-19 prediction was proposed using personality traits. Researchers conducted a comprehensive study of Qatar and accessed various healthcare records arising from COVID-19 (Khanra et al., 2020; Ma et al., 2018). Eminent researchers proposed a system for a hospital infrastructure management system. They surveyed designing a comprehensive system covering multispecialty domains (De Silva et al., 2015; Moutselos et al., 2018). Some researchers designed a diagnostic analytics system for heart failure readmission cases for heart patients’ readmission based on pulmonary infection using naïve Bayes theorem. They achieved 71% accuracy in probability assessment (Gowsalya et al., 2014; Navaz et al., 2018; Wu et al., 2017). Researchers carried out an extrinsic survey on various healthcare data using SVM and Neural Network for predictions of heart stroke using ECG signals (Sabharwal et al., 2016; Salomi & Balamurugan, 2016). Figure 1 points to the Big Data with its dimensions for identification of patient’s voluminous data.
Key Terms in this Chapter
Machine Learning: Machine learning is a tool used in health care to help medical professionals care for patients and manage clinical data. It is an application of artificial intelligence, which involves programming computers to mimic how people think and learn.
Deep Learning: Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data
Model Validation: It is a phase of machine learning that quantifies the ability of an ML or statistical model to produce predictions or outputs with enough fidelity to be used reliably to achieve business objectives.
Ayushman Bharat -National Health Protection Mission (AB-NHPM): Ayushman Bharat Yojana (ABY) is a central government-funded free healthcare coverage scheme. The scheme is focused on nearly 11 crore poor and vulnerable families in rural and urban India. It is the largest scheme of its kind in the world. ABY envisions a two-pronged, unified approach by both government and private hospitals, to provide a comprehensive healthcare on primary, secondary and tertiary levels. This is planned to be accomplished through Health and Wellness Centres (HWCs) and Pradhan Mantri Jan Arogya Yojana (PM-JAY).
Support Vector Machine: A support vector machine (SVM) is a type of deep learning algorithm that performs supervised learning for classification or regression of data groups. In AI and machine learning, supervised learning systems provide both input and desired output data, which are labelled for classification.
Model Testing: In machine learning, model testing is referred to as the process where the performance of a fully trained model is evaluated on a testing set. The testing set consisting of a set of testing samples should be separated from the both training and validation sets, but it should follow the same probability distribution as the training set. Each testing sample has a known value of the target. Based on the comparison of the model’s predicted value, and the known target, for each testing sample, the performance of the trained model can be measured. There are a number of statistical metrics that can be used to assess testing results including mean squared errors and receiver operating characteristics curves. The question of which one should be used is largely dependent on the type of models and the type of application. For a regression (Regression Analysis) model, the standard error of estimate is widely used.
Particle Swarm Optimization (PSO): It is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems.