Optimizing the long short-term memory algorithm to improve the accuracy of infectious diseases prediction

Abstract

<p><span lang="EN-US">This study discusses the implementation of the proposed optimized</span><span lang="EN-US">long short-term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significant difference in the average prediction results with real cases between the three models. The main objectives of this study were: </span><span lang="EN-US">modifying the LSTM algorithm to predict the number of infectious disease cases to get a smaller residual value</span><span lang="EN-US">, <span>comparing the results of the optimization accuracy of the LSTM algorithm with the LSTM algorithm in previous studies, and evaluating the use of spatial variables in applying infectious disease prediction models using the LSTM algorithm. The results found that the performance difference between the proposed optimization algorithm and the model in the previous study was obtained. The proposed LSTM optimization algorithm had an accuracy improvement of about 2% over the previous model.</span></span></p>