Indonesia tuberculosis morbidity rate forecasting using recurrent neural network
D. Harlianto, Siti Mardiyati, Dian Ika Lestari, Arman Haqqi Anna Zili, Sindy Devila · AIP conference proceedings · 2020
Several insurance companies sell health insurance products that cover tuberculosis risk. One principal component to determine the insurance premium that must be paid by the insured is the morbidity rate. Therefore, morbidity rate forecasting is essential for an insurance company. In this paper, we present the Indonesia tuberculosis morbidity rate forecasting using Recurrent Neural Network (RNN) which is part of deep learning. Min-Max Scaler was applied to the data before it is used as RNN input to achieve better prediction. Unfortunately, the result shows that RNN performance is not satisfactory due to limited morbidity rate data in Indonesia.