• Nilambari Arvind Jagtap
• Tushar D. Kshirsagar
• Hrishikesh A. Gujar
• Nikhil S. Bhoite

In this research we have proposed an innovative machine learning model to predict recovery time of patient using predictive and prescriptive analysis. Recently, the healthcare industry has started using inventory management applications to achieve efficiency and effectiveness in its supply chains. There is a rapid growth in the demand of drugs and diagnostic systems within the healthcare industry. The biggest challenge for health care supply chains is to manage inventory efficiently and keep up the satisfactory service level at the same time. Our objective of this study is to build a system to manage the inventories in hospital wards using Predictive and Prescriptive Analysis with the help of various machine learning techniques and to conclude which techniques are effective and accurate. A secondary data is collected from four different hospitals named Chaitanya Hospital Baramati, Shripal Hospital Baramati, Bhagat Hospital Baramati, Bhagyajay Hospital Baramati. Modern Machine learning algorithms such as Random Forest, Gradient boosting, Ada boosting, KNN classifier, Decision Tree etc. are used to classify this dataset. The accuracy of the model is calculated and then the one with good accuracy is taken as the model for predicting recovery time. The model we propose makes use of machine learning by taking into consideration both historical usage patterns of the ward’s current situation to minimize inventory levels as well as the necessity for emergency.

Prescriptive Model, Machine learning, Beta Blockers, Abdominal Disease, Random Forest, Decision Tree

"PREDICTIVE AND PRESCRIPTIVE STATISTICAL MODELS FOR INVENTORY MANAGEMENT IN HEALTH CARE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.h92-h96, January-2024, Available :http://www.jetir.org/papers/JETIR2401710.pdf

"PREDICTIVE AND PRESCRIPTIVE STATISTICAL MODELS FOR INVENTORY MANAGEMENT IN HEALTH CARE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. pph92-h96, January-2024, Available at : http://www.jetir.org/papers/JETIR2401710.pdf