Comparison of The Dempster Shafer Method and Bayes' Theorem in The Detection of Inflammatory Bowel Disease
Abstract views: 145
,
PDF downloads: 111
Abstract
This study discusses the comparison of the Dempster-Shafer method and Bayes' theorem in the process of early detection of inflammatory bowel disease. Inflammatory bowel disease, better known as intestinal inflammation, attacks the digestive tract in the form of irritation, chronic inflammation, and injuries to the digestive tract. Early signs of inflammatory bowel disease include excess abdominal pain, blood when passing stools, acute diarrhea, weight loss, and fatigue. The Dempster-Shafer method is a method that produces an accurate diagnosis of uncertainty caused by adding or reducing information about the symptoms of a disease. Meanwhile, Bayes' theorem explains the probability of an event based on the factors that may be related to the event. This study aims to measure the accuracy of disease detection using the Dempster-Shafer method compared to the probability of occurrence of the disease using Bayes' theorem. The results of calculating the level of accuracy show that the Bayes Theorem method is better at predicting inflammatory bowel disease with a probability of occurrence of disease in the tested data of 75.9%.
References
D. Wang et al., “Influence of sleep disruption on inflammatory bowel disease and changes in circadian rhythm genes,” Heliyon, vol. 8, no. 10, 2022, doi: 10.1016/j.heliyon.2022.e11229.
A. Armuzzi et al., “Female reproductive health and inflammatory bowel disease: A practice-based review,” Dig. Liver Dis., vol. 54, no. 1, pp. 19–29, 2022, doi: 10.1016/j.dld.2021.05.020.
Y. Chen et al., “The treatment of inflammatory bowel disease with monoclonal antibodies in Asia,” Biomed. Pharmacother., vol. 157, no. November 2022, p. 114081, 2023, doi: 10.1016/j.biopha.2022.114081.
J. Zhang et al., “m6A modification in inflammatory bowel disease provides new insights into clinical applications,” Biomed. Pharmacother., vol. 159, no. January, p. 114298, 2023, doi: 10.1016/j.biopha.2023.114298.
D. Dohos et al., “Inflammatory bowel disease does not alter the clinical features and the management of acute pancreatitis: A prospective, multicentre, exact-matched cohort analysis,” Pancreatology, vol. 22, no. 8, pp. 1071–1078, 2022, doi: 10.1016/j.pan.2022.09.241.
B. N. Limketkai et al., “Dietary Interventions for the Treatment of Inflammatory Bowel Diseases: An Updated Systematic Review and Meta-analysis,” Clin. Gastroenterol. Hepatol., 2023, doi: 10.1016/j.cgh.2022.11.026.
L. P. Wanti and S. Romadlon, “Implementasi Forward Chaining Method Pada Sistem Pakar Untuk Deteksi Dini Penyakit Ikan,” Infotekmesin, vol. 11, no. 02, pp. 74–79, 2020, doi: 10.35970/infotekmesin.v11i2.248.
L. P. Wanti, I. N. Azroha, and M. N. Faiz, “Implementasi User Centered Design Pada Sistem Pakar Diagnosis Gangguan Perkembangan Motorik Kasar Pada Anak Usia Dini,” Media Apl., vol. 11, no. 1, pp. 1–10, 2019.
L. P. Wanti and Lina Puspitasari, “Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 16–24, 2022, doi: 10.29207/resti.v6i1.3599.
I. Bahroni, L. P. Wanti, N. W. Rahadi, A. A. Hartono, and R. Purwanto, “Implementation of Forward Chaining for Diagnosis of Dengue Hemorrhagic Fever,” J. Innov. Inf. Technol. Appl., vol. 4, no. 1, pp. 32–42, 2022, doi: 10.35970/jinita.v4i1.1204.
K. Kirman, A. Saputra, and J. Sukmana, “Sistem Pakar Untuk Mendiagnosis Penyakit Lambung Dan Penanganannya Menggunakan Metode Dempster Shafer,” Pseudocode, vol. 6, no. 1, pp. 58–66, 2019, doi: 10.33369/pseudocode.6.1.58-66.
C. Nas, “Sistem Pakar Diagnosa Penyakt Tiroid Menggunakan Metode Dempster Shafer,” J. Teknol. Dan Open Source, vol. 2, no. 1, pp. 1–14, 2019, doi: 10.36378/jtos.v2i1.114.
A. A. S. Nugraha, N. Hidayat, and L. Fanani, “Sistem Pakar Diagnosis Penyakit Kucing Menggunakan Metode Naive Bayes – Certainty Factor Berbasis Android,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 2, pp. 650–658, 2018.
Y. Wiguna, F. Taufik, and A. H. Nasyuha, “Sistem Pakar Mendiagnosa Penyakit Batu Karang Menggunakan Metode Dempster Shafer,” J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 5, no. 1, p. 66, 2022, doi: 10.53513/jsk.v5i1.4793.
R. Ardiansyah, F. Fauziah, and A. Ningsih, “Sistem Pakar Untuk Diagnosa Awal Penyakit Lambung Menggunakan Metode Dempster-Shafer Berbasis Web,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 182–196, 2019, doi: 10.35760/tr.2019.v24i3.2395.
D. Aldo, “Sistem Pakar Diagnosis Hama Dan Penyakit Bawang Merah Menggunakan Metode Dempster Shafer,” Komputika J. Sist. Komput., vol. 9, no. 2, pp. 85–93, 2020, doi: 10.34010/komputika.v9i2.2884.
J. Kanggeraldo, R. P. Sari, and M. I. Zul, “Sistem Pakar Untuk Mendiagnosis Penyakit Stroke Hemoragik dan Iskemik Menggunakan Metode Dempster Shafer,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 2, pp. 498–505, 2018, doi: 10.29207/resti.v2i2.268.
A. Rosana, G. Pasek, S. Wijaya, and F. Bimantoro, “Sistem Pakar Diagnosa Penyakit Kulit pada Manusia dengan Metode Dempster Shafer (Expert System of Diagnosing Skin Disease of Human being using Dempster Shafer Method),” J-Cosine, vol. 4, no. 2, pp. 129–138, 2020, [Online]. Available: http://jcosine.if.unram.ac.id/.
W. A. Van Eeden et al., “Predicting the 9-year course of mood and anxiety disorders with automated machine learning : A comparison between auto-sklearn , naïve Bayes classifier , and traditional logistic regression,” Psychiatry Res., vol. 299, no. October 2020, p. 113823, 2021, doi: 10.1016/j.psychres.2021.113823.
A. P. Wibawa et al., “Naïve Bayes Classifier for Journal Quartile Classification,” Int. J. Recent Contrib. from Eng. Sci. IT, vol. 7, no. 2, p. 91, 2019, doi: 10.3991/ijes.v7i2.10659.
S. Shastri et al., “Development of a Data Mining Based Model for Classification of Child Immunization Data,” Int. J. Comput. Eng. Res., vol. 8, no. 6, pp. 41–49, 2018, [Online]. Available: www.ijceronline.com.
O. Somantri, R. H. Maharrani, and L. P. Wanti, “An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes,” Telematika, vol. 15, no. 1, pp. 14–22, 2022, doi: 10.35671/telematika.v15i1.1307.
S. H. Alizadeh, A. Hediehloo, and N. Shiri, “Knowledge-Based Systems Multi independent latent component extension of naive Bayes classifier,” Knowledge-Based Syst., vol. 213, p. 106646, 2021, doi: 10.1016/j.knosys.2020.106646.
A. Saleh and F. Nasari, “Penerapan Equal-Width Interval Discretization Dalam Metode Naive Bayes Untuk Meningkatkan Akurasi Prediksi Pemilihan Jurusan Siswa,” Masy. Telemat. Dan Inf. J. Penelit. Teknol. Inf. dan Komun., vol. 9, no. 1, p. 1, 2018, doi: 10.17933/mti.v9i1.113.
N. Sulardi and A. Witanti, “SISTEM PAKAR UNTUK DIAGNOSIS PENYAKIT ANEMIA MENGGUNAKAN,” vol. 1, no. 1, pp. 19–24, 2020.
D. Santra, S. K. Basu, J. K. Mandal, and S. Goswami, “Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study,” Expert Syst. Appl., vol. 145, p. 113084, 2020, doi: 10.1016/j.eswa.2019.113084.
J. Yuan, S. Zhang, S. Wang, F. Wang, and L. Zhao, “Process abnormity identification by fuzzy logic rules and expert estimated thresholds derived certainty factor,” Chemom. Intell. Lab. Syst., vol. 209, no. August 2020, p. 104232, 2021, doi: 10.1016/j.chemolab.2020.104232.
S. Dai et al., “SeDeM expert system for directly compressed tablet formulation: A review and new perspectives,” Powder Technol., vol. 342, pp. 517–527, 2019, doi: 10.1016/j.powtec.2018.10.027.
Copyright (c) 2024 Linda Perdana Wanti, Eka Tripustikasari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
