Leveraging Random Forest and LSTM Models for Enhanced Disease Outbreak Prediction Using Machine Learning

Abstract

This research paper investigates the application of machine learning techniques, specifically Random Forest (RF) and Long Short-Term Memory (LSTM) models, to enhance the prediction accuracy of disease outbreaks. Traditional epidemiological models often struggle with the inherent complexity and non-linearity present in disease spread patterns. To address these challenges, we propose a hybrid approach that leverages the strengths of both RF and LSTM models. The RF model is employed to handle high-dimensional feature spaces and to perform feature selection, providing a robust mechanism for identifying key predictors of disease outbreaks. In parallel, the LSTM model is utilized to capture temporal dependencies and non-linear patterns in the time-series data, offering a dynamic understanding of disease progression. Our dataset comprises multiple sources, including historical disease records, environmental factors, and socio-economic indicators, ensuring a comprehensive analysis. The proposed hybrid model is evaluated against standard benchmarks on several disease datasets, showing superior performance in terms of prediction accuracy, recall, and precision. Additionally, we conduct a sensitivity analysis to assess the impact of various features on the model's predictive capability, leading to actionable insights for public health interventions. The results underscore the potential of integrating RF and LSTM models to improve early warning systems for disease outbreaks, ultimately aiding in more effective resource allocation and proactive healthcare planning.