Modelling and Forecasting the Dengue Hemorrhagic Fever Cases Number Using Hybrid Fuzzy-ARIMA

被引:6
|
作者
Anggraeni, Wiwik [1 ]
Abdillah, Abdolatul [1 ]
Pujiadi [2 ]
Trikoratno, Lulus Tjondro [3 ]
Wibowo, Radityo Prasetianto [1 ]
Purnomo, Mauridhi Hery [4 ]
Sudiarti, Yeyen [1 ]
机构
[1] Inst Teknol Sepuluh Nopember, Dept Informat Syst, Surabaya, Indonesia
[2] Malang Regency Publ Hlth Off, Dengue Fever Eradicat, Malang, Indonesia
[3] Malang Regency Publ Hlth Off, Dis Prevent & Eradicat, Malang, Indonesia
[4] Inst Teknol Sepuluh Nopember, Dept Comp Engn, Dept Elect Engn, Surabaya, Indonesia
来源
2019 IEEE 7TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH (SEGAH) | 2019年
关键词
modelling; forecasting; dengue hemorrhagic fever; fuzzy inference system; ARIMA; hybrid; TIME-SERIES ANALYSIS; SAO-PAULO; INFECTION; STATE;
D O I
10.1109/segah.2019.8882433
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Dengue Hemorrhagic Fever (DHF) cases in Indonesia have the highest number which compared to another countries in Southeast Asia. These occurs in almost all part of Indonesia including East Java, especially Malang Regency. In 2016, Malang Regency was listed as the top three regions with the highest number of DHF cases in East Java. Some actions have been done by the Regional Government and Malang Regency Public Health Office to push the occurrence of this case but the results obtained are not optimal yet. It needs the results of the DHF cases number forecasting so that early prevention of disease growth and the emergence of an outbreak can be carried out. The goal of this research are make model and forecast the number of DHF cases in Malang Indonesia. The area in Malang Regency is divided into three parts, namely lowlands, middlelands and highlands. Samples were taken from each region to get a suitable model. The results of the research in each region show that the proposed hybrid method has an accuracy that is not significantly different compared to without hybrid. The average difference in the value of SMAPE is 0.48% with the details of the average difference in SMAPE in lowlands is 1.72%, middlelands is 0.51%, and highlands is 0.22%.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm
    Barak, Sasan
    Sadegh, S. Saeedeh
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2016, 82 : 92 - 104
  • [22] Land Use Changes and Cluster Identification of Dengue Hemorrhagic Fever Cases in Bandung, Indonesia
    Sari, Sri Yusnita Irda
    Adelwin, Yessika
    Rinawan, Fedri Ruluwedrata
    TROPICAL MEDICINE AND INFECTIOUS DISEASE, 2020, 5 (02)
  • [23] Forecasting the number of Arab and foreign tourists in Egypt using ARIMA models
    Ismail, Eman Ahmed Aly
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2020, 11 (02) : 450 - 454
  • [24] Forecasting the number of Arab and foreign tourists in Egypt using ARIMA models
    Eman Ahmed Aly Ismail
    International Journal of System Assurance Engineering and Management, 2020, 11 : 450 - 454
  • [25] Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models
    Busari, S. I.
    Samson, T. K.
    SCIENTIFIC AFRICAN, 2022, 18
  • [26] Influence of Rainfall and Logistic Factors on Dengue Hemorrhagic Fever Cases in Mountainous Areas in Yogyakarta, Indonesia
    Kesetyaningsih, Tri Wulandari
    Kusbaryanto
    Widayani, Prima
    Listyaningrum, Noviyanti
    BANGLADESH JOURNAL OF MEDICAL SCIENCE, 2025, 24 (01): : 246 - 255
  • [27] Dengue Hemorrhagic Fever (DHF) Cases in Semarang City are Related to Air Temperature, Humidity, and Rainfall
    Widyorini, Prasti
    Shafrin, Kintan Arifa
    Wahyuningsih, Nur Endah
    Murwani, Retno
    Suhartono
    ADVANCED SCIENCE LETTERS, 2017, 23 (04) : 3283 - 3287
  • [28] Time series forecasting model using a hybrid ARIMA and neural network
    Zou, Haofei
    Yang, Fangfing
    Xia, Guoping
    PROCEEDINGS OF THE 2005 CONFERENCE OF SYSTEM DYNAMICS AND MANAGEMENT SCIENCE, VOL 2: SUSTAINABLE DEVELOPMENT OF ASIA PACIFIC, 2005, : 934 - 939
  • [29] Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM
    Dave, Emmanuel
    Leonardo, Albert
    Jeanice, Marethia
    Hanafiah, Novita
    5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMPUTATIONAL INTELLIGENCE 2020, 2021, 179 : 480 - 487
  • [30] Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
    Mustaffa, Nurakmal Ahmad
    Zahari, Siti Mariam
    Farhana, Nor Alia
    Nasir, Noryanti
    Azil, Aishah Hani
    INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES, 2024, 11 (01): : 20 - 31