A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection

TB/HIV coinfection poses a complex public health challenge. Accurate forecasting of future trends is essential for efficient resource allocation and intervention strategy development. This study compares classical statistical and machine learning models to predict TB/HIV coinfection cases stratified by gender and the general populations. We analyzed time series data using exponential smoothing and ARIMA to establish the baseline trend and seasonality. Subsequently, machine learning models (SVR, XGBoost, LSTM, CNN, GRU, CNN-GRU, and CNN-LSTM) were employed to capture the complex dynamics and inherent non-linearities of TB/HIV coinfection data. Performance metrics (MSE, MAE, sMAPE) and the Diebold-Mariano test were used to evaluate the model performance. Results revealed that Deep Learning models, particularly Bidirectional LSTM and CNN-LSTM, significantly outperformed classical methods. This demonstrates the effectiveness of Deep Learning for modeling TB/HIV coinfection time series and generating more accurate forecasts.

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Publication Details

Journal
Scientific Reports
Published
2024-08-16
DOI
https://doi.org/10.1038/s41598-024-69580-4
Citations
16
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
5.56

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article

A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection

Thaís Zamboni Berra, Antônio Carlos Vieira Ramos, Alessandro Rolim Scholze, Lucas Faria Porto et al.
16 citations
Scientific Reports
Machine Learning in Healthcare
5.56
article

A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection

Thaís Zamboni Berra, Antônio Carlos Vieira Ramos, Alessandro Rolim Scholze, Lucas Faria Porto, Nathan da Silva Barros, André Abade, Ricardo Alexandre Arcêncio, Josilene Dália Alves, Daniely Kuntath
article en
16 citations

Abstract

TB/HIV coinfection poses a complex public health challenge. Accurate forecasting of future trends is essential for efficient resource allocation and intervention strategy development. This study compares classical statistical and machine learning models to predict TB/HIV coinfection cases stratified by gender and the general populations. We analyzed time series data using exponential smoothing and ARIMA to establish the baseline trend and seasonality. Subsequently, machine learning models (SVR, XGBoost, LSTM, CNN, GRU, CNN-GRU, and CNN-LSTM) were employed to capture the complex dynamics and inherent non-linearities of TB/HIV coinfection data. Performance metrics (MSE, MAE, sMAPE) and the Diebold-Mariano test were used to evaluate the model performance. Results revealed that Deep Learning models, particularly Bidirectional LSTM and CNN-LSTM, significantly outperformed classical methods. This demonstrates the effectiveness of Deep Learning for modeling TB/HIV coinfection time series and generating more accurate forecasts.

Scientific ReportsVol. 14(1)
Universidade de Ribeirão Preto (BR), Universidade de São Paulo (BR), Universidade do Estado de Minas Gerais (BR), Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso (BR), Universidade Federal de Mato Grosso (BR), Universidade Estadual do Norte do Paraná (BR)
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Mato Grosso, Universidade Federal de Mato Grosso do Sul
Good health and well-being
Openalex Percentile: Top 4%
Machine Learning in Healthcare
5.56
DOIOpen Access PDFOpenAlex
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