• SANDEEP RAVIKANTI
• Mohammed Abdul Hai Siddiquie
• Syed Noor ul Mustafa
• Mohammad Ikramuddin

Healthcare costs and the increased demand for services especially during corona pandemic time we are facing lot of havoc which requires us to use healthcare resources and hospitalization of patients more efficiently. Static resource requirements and stay duration makes the care delivery process less efficient. We can create dynamic system by classifying patients into similar clusters by predicting stay. Developed a classification model to classify patients into various clusters of stay by using e-patients’ record. There are various statistical tools for classification and prediction. However, classification and regression tree (CART) analysis is a more suitable method for analyzing healthcare data. We found that the CART analysis is also useful for determining the patient attributes that can explain the variability in resource requirements. Furthermore, we can predict the stay duration of patients based on certain factors, such as the age, the admission point, severity, emergency type and the disease type.

ICU, LoS, Machine Learning, Data mining, CART, Bernoulli

"CLASSIFICATION MODEL TO PREDICT DYNAMIC HEALTHCARE RESOURCE UTILIZATION AND ALLOTMENT METHOD BY USING CART ANALYSIS FOR SURGICAL PATIENTS LOS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.e204-e209, May-2021, Available :http://www.jetir.org/papers/JETIR2105565.pdf

"CLASSIFICATION MODEL TO PREDICT DYNAMIC HEALTHCARE RESOURCE UTILIZATION AND ALLOTMENT METHOD BY USING CART ANALYSIS FOR SURGICAL PATIENTS LOS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppe204-e209, May-2021, Available at : http://www.jetir.org/papers/JETIR2105565.pdf