• Shiful Islam Rony
• Rajib Chandra Ghosh
• Shuvo Nandi
• Salman Pasha
• C R Manjunath

The provision of pharmaceutical drugs in quantities accurate to consumption is an crucial point in the pharmaceutical industry and storage of medicines, as the production of large quantities of unnecessary drugs cause to a longer storage of drugs which is worthless. In the meantime most of the medicines have a short storage life.. A recurrent neural network with Long-Short Term Memory LSTM has been used by deep learning. The proposed methodology is founded on the seasonal number of prescription required quantities with the number of quarters as indicators. The purpose of this research is to forecast the drugs amount needed for one year. The proposed method is evaluated using two types of evaluation. The first one is based on MSE and the visualization of the actual data and forecasted data. The proposed method has reached a low value of MSE and the visualization graph is semi-identical, whereas the second evaluation method compares the result of the proposed method with traditional forecasting method. Multiple linear regression is a traditional prediction method used with the data set, whose results are relatively good and promising compared to the results of the traditional method.

Drugs consumption forecasting, DNN, LSTM, Recurrent long-short term memory-deep learning based drug analysis and forecasting, RNN

"Prediction Of Medicine Consumption Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.d68-d73, May-2021, Available :http://www.jetir.org/papers/JETIR2105391.pdf

"Prediction Of Medicine Consumption Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppd68-d73, May-2021, Available at : http://www.jetir.org/papers/JETIR2105391.pdf