ENHANCING HEALTHCARE INDUSTRIAL APPLICATIONS WITH LSTM-BASED PREDICTIVE ANALYTICS

Abstract

Aim/Purpose This work attempts to investigate the application of Long Short Term Memory (LSTM)-based predictive analytics in the medical field. The scope comprises the development and application of LSTM models to forecast outcomes, including patient diagnosis, treatment responses, healthcare resource consumption, and other relevant variables. Background Predictive analytics has become popular in many other industries, including healthcare, since it can analyze enormous amounts of data and project future patterns. For LSTM, a variant of encoder-decoder LSTM-based recurrent neural network (RNN), time-series prediction activities have demonstrated superior performance. Accurate projections in the medical industry can lead to better pa-tient outcomes, optimal use of resources, and cost cuts. Methodology The method calls for compiling and preparing medical data from numerous sources. Using prior data, LSTM models will learn temporal patterns and rela-tionships between target outcomes and input variables. Several techniques, in-cluding feature engineering, hyperparameter tuning, and model evaluation, will be employed to maximize LSTM-based predictive analytics. Contribution This work supports the growth of predictive analytics in the healthcare industry by demonstrating the accuracy of LSTM models in forecasting significant clinical outcomes. This research could change healthcare decision-making processes, improving general operational effectiveness, patient treatment, and resource use. Findings Among other healthcare outputs, the results suggest that LSTM-based predic-tive analytics can faithfully project patient diagnosis, illness progression, and therapy responses. The models outperform traditional forecasting methods over multiple datasets in terms of reliability and accuracy. Recommendations for Researchers Future studies can focus on bettering model designs, leveraging various data modalities, and including predictive analytics in clinical decision support sys-tems. Cooperation among data scientists, medical professionals, and legislators will help to realize the possibilities of LSTM-based predictive analytics in healthcare. Future Research This work can be enhanced in future research using several deep learning algo-rithms with a real-time industrial dataset.