Mortality Prediction using Machine Learning Techniques: Comparative Analysis

• Main Authors: Verma, Akash, Goyal, Shreya, Thakur, Shridhar Kumar, Gupta, Archit, Gupta, Indrajeet
• Format: Conference Proceeding
• Language: English
• Subjects: Computational modeling; Feature Extraction; Feature Scaling; Hospitals; Length measurement; Logistics Regression; Machine Learning Algorithm; Machine learning algorithms; Neural Networks; Prediction algorithms; Predictive models; Support Vector Machine; Support vector machines
• ISSN: 2473-3571
• DOI: 10.1109/IACC48062.2019.8971566

In recent past, data mining, artificial intelligence, and machine learning have gained enormous attention to improve hospital performance. In some hospitals, medical personals want to improve their statists by decreasing the number of patients dying in the hospital. The research is focused on the mortality prediction of measurable outcomes, including the risk of complications & length of hospital stay. The duration spent in the hospital of the patient plays an important role both for patients & healthcare providers, influenced by numerous factors. LOS (length of stay) in critical care has great importance, both to the patient experience as well as the cost of care and is influenced by the complex environmental factors of the Hospitals. LOS is a parameter that is used to identify the extremity of illness & health-related resource utilization. This paper provides the improved prediction rate that a patient survives or dies in the range of length of stay in the hospital. It also anchors the analytical methods for the length of stay and mortality prediction.