• Prathima Kebbepura R C
• Rakesh S

Effective patient emergency management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all the patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efficient treatment plan and know the predicted waiting time through a mobile application that updates in real time. Therefore, we use machine learning algorithms in predicting the risk of admission from the Emergency department. We use two algorithms to build the predictive models: (1) Naïve bayes (2) decision trees. This study highlights the potential utility of two common machine learning algorithms in predicting patient admissions. Practical implementation of the models developed in this study in decision support tools would provide a snapshot of predicted admissions from the emergency department at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates.

Machine learning, Predictive models, Hospital.

"PREDICTION OF TIME FOR PATIENT IN A HOSPITAL USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.391-397, June 2019, Available :http://www.jetir.org/papers/JETIR1906O58.pdf

"PREDICTION OF TIME FOR PATIENT IN A HOSPITAL USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp391-397, June 2019, Available at : http://www.jetir.org/papers/JETIR1906O58.pdf