A Comparative Study of Statistical (SARIMA) Vis-À-Vis Some Traditional Machine-Learning and Deep-Learning Techniques to Forecast Malaria Incidences in Kolkata of India
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| Názov: | A Comparative Study of Statistical (SARIMA) Vis-À-Vis Some Traditional Machine-Learning and Deep-Learning Techniques to Forecast Malaria Incidences in Kolkata of India |
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| Autori: | Krishnendra Sankar Ganguly, Krishna Sankar Ganguly, Ambar Dutta |
| Zdroj: | International Journal of Information Technology and Computer Science. 17:68-83 |
| Informácie o vydavateľovi: | MECS Publisher, 2025. |
| Rok vydania: | 2025 |
| Popis: | To augment the accuracy of the results of a Time-Series Forecasting problem in the Computational Epidemiology domain of Public Health, to generate an accurate alert in a Real-time Outbreak and Disease Surveillance (RODS) system, namely in the prediction of Malaria incidences, an interdisciplinary approach of data analysis [through Statistical along with Machine-Learning (ML) and Deep-Learning techniques (DL)] has been studied in this research. Two different Non-linear Deep-Learning based techniques, viz., Long Short-Term Memory (LSTM) [a subclass of Recurrent Neural Network (RNN)] & Gated Recurrent Unit (GRU) and two different Non-linear Machine-Learning techniques, viz., Random Forest Regressor & Non-linear Support Vector Machine Regressor are applied in this study to compare against the traditional Statistical-based linear SARIMA model, to forecast a longitudinal data-set of malaria incidences. While SARIMA or other traditional Autoregressive (AR) models, necessitating a smaller number of parameters, undergo limited training and limited prediction power, ML and DL models show profound and persistent performance improvement with better noise-handling/ missing values and perform multi-step forecasts. Moreover, the over-fitting issue can be combated by introducing densely connected residual links in the ML/ DL networks. |
| Druh dokumentu: | Article |
| ISSN: | 2074-9015 2074-9007 |
| DOI: | 10.5815/ijitcs.2025.05.06 |
| Prístupové číslo: | edsair.doi...........197b702195c2997b55b0eaede867d9f5 |
| Databáza: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | To augment the accuracy of the results of a Time-Series Forecasting problem in the Computational Epidemiology domain of Public Health, to generate an accurate alert in a Real-time Outbreak and Disease Surveillance (RODS) system, namely in the prediction of Malaria incidences, an interdisciplinary approach of data analysis [through Statistical along with Machine-Learning (ML) and Deep-Learning techniques (DL)] has been studied in this research. Two different Non-linear Deep-Learning based techniques, viz., Long Short-Term Memory (LSTM) [a subclass of Recurrent Neural Network (RNN)] & Gated Recurrent Unit (GRU) and two different Non-linear Machine-Learning techniques, viz., Random Forest Regressor & Non-linear Support Vector Machine Regressor are applied in this study to compare against the traditional Statistical-based linear SARIMA model, to forecast a longitudinal data-set of malaria incidences. While SARIMA or other traditional Autoregressive (AR) models, necessitating a smaller number of parameters, undergo limited training and limited prediction power, ML and DL models show profound and persistent performance improvement with better noise-handling/ missing values and perform multi-step forecasts. Moreover, the over-fitting issue can be combated by introducing densely connected residual links in the ML/ DL networks. |
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| ISSN: | 20749015 20749007 |
| DOI: | 10.5815/ijitcs.2025.05.06 |