The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland

• Main Author: Mohamed Yusuf Hassan
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Symmetry
• Subjects: artificial intelligence; training data; Pearson correlation; Dickey–Fuller test; long short-term memory; machine learning
• Online Access: https://www.mdpi.com/2073-8994/13/7/1156
• ISSN: 2073-8994

The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.