Predictive Analysis on Medicines Availability in Hospitals Using Machine Learning and Deep Learning Technique

Predictive analytics is an essential tool for optimizing hospital inventory management, especially for the availability of essential medicines. Accurate forecasting of medicine demand can reduce the risk of shortages and overstocking, leading to cost savings and improved patient care. This project explores the use of machine learning algorithms, specifically Random Forest, Decision Tree, and Convolutional Neural Networks (CNN), to predict the availability of medicines in hospitals. These algorithms analyze historical medicine usage data and incorporate external factors such as disease outbreaks, seasonal fluctuations, and hospital admission rates to predict future demand. The implementation of these models aims to optimize the hospital’s medicine supply chain by providing accurate forecasts for inventory management. The results show that machine learning based predictions significantly improve the accuracy of medicine availability forecasts, helping healthcare providers make informed decisions regarding stock levels.