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All Journal Jurnal Ilmiah Kursor International Journal of Advances in Intelligent Informatics CESS (Journal of Computer Engineering, System and Science) Paradigma Jurnal Teknik Informatika UNIKA Santo Thomas JATI (Jurnal Mahasiswa Teknik Informatika) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Patria : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Applied Data Sciences Jurnal Abdimas Bina Bangsa Jurnal Media Informatika Jurnal Penelitian Inovatif Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Pada Masyarakat Indonesia (JPPMI) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Archive: Jurnal Pengabdian Kepada Masyarakat Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Sosial Increate
Yovinia Carmeneja Hoar Siki
Ilmu Komputer, Fakultas Teknik, Universitas Katolik Widya Mandira, Indonesia
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Journal : Journal of Applied Data Sciences

 1 
Determining Important Features for Dengue Diagnosis using Feature Selection Methods Bria, Yulianti Paula; Nani, Paskalis Andrianus; Siki, Yovinia Carmeneja Hoar; Mamulak, Natalia Magdalena Rafu; Meolbatak, Emiliana Metan; Guntur, Robertus Dole
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.445

Abstract

This research aims to determine the important features including symptoms and risk factors for dengue diagnosis. This study’s dataset was obtained from medical records collected from two hospitals in Indonesia from patients with dengue and nondengue diseases. Four feature selection methods including feature importance, recursive feature elimination, correlation matrix and KBest were leveraged to determine significant features. Feature importance employed a tree-based classifier to derive the importance scores of the features. Recursive feature elimination employed a machine learning classifier to choose the most important features from the given dataset. Correlation matrix was employed to select the best features because it has the ability to use the correlation between each feature with the target. Univariate feature selection – Kbest has the ability to choose the best features based on univariate statistical tests. Important features were also gathered from fifteen Indonesian medical doctors to confirm the results. We used six machine learning techniques for dengue prediction. The random forest classifier yields the highest accuracy for the best combination of features with the accuracy of 0.93 (LR: 0.90 (0.04), KNN: 0.89 (0.04), XGBoost: 0.91 (0.03), RF: 0.93 (0.04), NB: 0.88 (0.09), SVM: 0.89 (0.04)) and precision of 0.90 (LR: 0.86 (0.22), KNN: 0.67 (0.14), XGBoost: 0.77 (0.13), RF: 0.90 (0.13), NB: 0.66 (0.20), SVM: 0.66 (0.18)). This study shows the significant features for dengue diagnosis including fever, fever duration, headache, muscle and joint pain, nausea, vomiting, abdominal pain, shivering, malaise, loss of appetite, shortness of breath, rash, bleeding nose, bitter mouth, temperature and age. This knowledge is pivotal to educate society to seek medical advice when dengue symptoms appear to avoid severe conditions. Arthralgia/joint pain and myalgia/muscle pain are the most significant features for the dengue prediction. This knowledge is important for medical doctors as a starting point for clinical dengue diagnosis.
Co-Authors Adri Gabriel Sooai Alfredo Abnit, Godefridus Alfry Aristo Jansen Sinlae Amfotis, David Andrianus Nani, Paskalis Angela Yunita Muti Anselmus Epi Jemagung Apelaby, Justinus Julio Krisna Bala, Yustinus Jubiliano Ebang Bana, Mariana Sonya Anjela Sanam Bano, Maria Bano, Mariana Bees, Azarya Bei, Intan Sulistik Benyamin Lily, Budy Bere, Maria Vianey Mega Bria, Yunita Mildayani Putri Bruno C.N. Sutal, Anjelique Ceriana Kuanaben Clara, Emerensiana Aprilia Daeng Bakka Mau, Sisilia David Amfotis Donatus Joseph Manehat Emanuel Jando Emerensiana Ngaga Emerensiana Ngaga Emerensiana Ngaga Emiliana Metan Meolbatak Eviana Nahak Ferdy Chanel D.rc Lay Frengky Tedy Frengky Tedy Gasperz, Elga Adeputra Hendrika Buik En Hokon, Sesilia Hingi Novita Sari Ignatius Pricher Agung Nirwanto Samane Imanuel Irvantus Nahak Joan Pierre Taolin Jose R . D. R X. Da Luz Joseph Manehat, Donatus Klotilda Olin Lalo, Aprilianus Kristianus Laurentino Da Costa Nunes Lediana Theresia Opat Maria Astiyani Nahak Maria J. Insantuan Maria Krisanti Ivoni Milo Maria Sinriana Maria Vianey Mega Bere Maximilianus Benge Meolbatak, Emiliana Metan Metkono, Beatrix Selia Mondolang, Alicia Herlin Naifio, Raynaldi Bouk Naikofi, Mauritius Ildo Rivendi Nani, Paskalis Adrianus Natalia Magdalena Rafu Mamulak Nikola Tolentini Nisa Aulia Pangu Loda, Januarya Paskalis Andrianus Nani Patrisius Batarius Pattiraja, Agustinus Harryanto Pattyraja, Agustinus Haryanto Paulina Aliandu Prasasto, Andrianus Robertus Dole Guntur Roselina Nanur Sabon Doni, Rikardus Sianturi, Shine Crossifixio Sisilia Daeng Bakka Mau Sisilia Daeng Bakka Mau Sisilia Daeng Bakka Mau Talo, Martinus Coreia Tokan, Thomas Boris Asalodan Tualaka, Nugrah Salmo William S. Na Yansen Nurak, Eugenius Yohan Yefta Novemphi Nahak Yohanes LIm, Irvan Yohanes Mada Masa Yulianti Paula Bria