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All Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Riosatria, Riosatria
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Computer Science & IT

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Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation Riosatria, Riosatria; Hairani, Hairani; Anggrawan, Anthony; Syahrir, Moch.
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4391

Abstract

The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation Riosatria, Riosatria; Hairani, Hairani; Anggrawan, Anthony; Syahrir, Moch.
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4391

Abstract

The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Co-Authors Anthony Anggrawan Hairani Hairani Syahrir, Moch.