A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume

TL;DR

This paper presents a CNN-LSTM deep learning model for accurately forecasting cancer drug sales volume using complex time series data, aiding pharmaceutical and healthcare planning.

Contribution

It introduces a hybrid CNN-LSTM framework specifically designed for predicting cancer drug sales, demonstrating improved accuracy over traditional models.

Findings

CNN-LSTM model achieved MSE of 1.150 and RMSE of 1.072.

Model effectively captures nonlinear and volatile sales patterns.

Provides technical support for data-driven healthcare decision-making.

Abstract

This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology and cancer treatment continue, the demand for oncology medications is steadily increasing. Accurate forecasting of cancer drug sales plays a critical role in optimizing production planning, supply chain management, and healthcare policy formulation. The dataset used in this research comprises quarterly sales records of a specific cancer drug in Egypt from 2015 to 2024, including multidimensional information such as date, drug type, pharmaceutical company, price, sales volume, effectiveness, and drug classification. To improve prediction accuracy, a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is employed. The CNN component is responsible for extracting local temporal features from the sales data, while the LSTM component captures long-term dependencies and trends. Model performance is evaluated using two widely adopted metrics: Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results demonstrate that the CNN-LSTM model performs well on the test set, achieving an MSE of 1.150 and an RMSE of 1.072, indicating its effectiveness in handling nonlinear and volatile sales data. This research provides theoretical and technical support for data-driven decision-making in pharmaceutical marketing and healthcare resource planning.