Comparing Fuzzy Logic Mamdani and Naí¯ve Bayes for Dental Disease Detection
Downloads
Background: Dental disease detection is essential for the diagnosis of dental diseases.
Objective: This research compares the Mamdani fuzzy logic and Naí¯ve Bayes in detecting dental diseases.
Methods: The first is to process data on dental disease symptoms and dental support tissues based on complaints of toothache consulted with experts at a community health centre (puskesmas). The second is to apply the Mamdani fuzzy logic and the Naí¯ve Bayes to the proposed expert system. The third is to provide recommended decisions about dental diseases based on the symptom data inputted into the expert system. Patient data were collected at the North Cilacap puskesmas between July and December 2021.
Results: The Mamdani fuzzy logic converts uncertain values into definite values, and the Naí¯ve Bayes method classifies the type of dental disease by calculating the weight of patients' answers. The methods were tested on 67 patients with dental disease complaints. The accuracy rate of the Mamdani fuzzy logic was 85.1%, and the Naí¯ve Bayes method was 82.1%.
Conclusion: The prediction accuracy was compared to the expert diagnoses to determine whether the Mamdani fuzzy logic method is better than the Naí¯ve Bayes method.
Keywords: Dental Disease, Expert System, Mamdani Fuzzy Logic, Naí¯ve Bayes, Prediction
A. Villa, J. da Costa, M. L. Duong, K. Frazier, and O. Urquhart, "Oral-systemic health considerations in dental settings: An American Dental Association Clinical Evaluators Panel survey,” J. Am. Dent. Assoc., vol. 153, no. 4, pp. 388-389.e2, 2022, doi: 10.1016/j.adaj.2022.01.006.
S. Isna Fitria Ali, R. Rizal Isnanto, and A. Budi Prasetijo, "Sistem Pakar Diagnosis Penyakit Difteri Menggunakan Logika Fuzzy,” pp. 89–104, 2020, [Online]. Available: https://dspace.uii.ac.id/handle/123456789/28778.
M. R. Manbait, F. Fankari, A. A. Manu, and E. Krisyudhanti, "Peran Orang Tua dalam Pemeliharaan Kesehatan Gigi dan Mulut,” Dent. Ther. J., vol. 1, no. 2, pp. 74–79, 2019, doi: 10.31965/dtl.v1i2.452.
P. C. Bots-VantSpijker, C. D. van der Maarel-Wierink, J. M. G. A. Schols, and J. J. M. Bruers, "Oral Health of Older Patients in Dental Practice: An Exploratory Study,” Int. Dent. J., vol. 72, no. 2, pp. 186–193, 2022, doi: 10.1016/j.identj.2021.05.003.
Kementerian Kesehatan RI, "Infodatin Kesehatan Gigi Nasional,” 2019.
P. Ananta Dama Putra, I. K. Adi Purnawan, and D. Purnami Singgih Putri, "Sistem Pakar Diagnosa Penyakit Mata dengan Fuzzy Logic dan Naí¯ve Bayes,” J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), vol. 6, no. 1, p. 35, 2018, doi: 10.24843/jim.2018.v06.i01.p04.
Y. Nurfarianti, "Sistem Pakar Untuk Diagnosis Dismenore Menggunakan Metode Naí¯ve Bayes,” Progr. Stud. Inform. Univ. Tanjungpura, vol. 4, no. 1, pp. 1–6, 2016.
F. Ekajaya, N. Hidayat, and M. Tri Ananta, "Diagnosis Penyakit THT Menggunakan Metode Fuzzy Tsukamoto Berbasis Android,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 10, pp. 2361–2365, 2018.
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.
E. Danish and M. Onder, "Application of Fuzzy Logic for Predicting of Mine Fire in Underground Coal Mine,” Saf. Health Work, vol. 11, no. 3, pp. 322–334, 2020, doi: 10.1016/j.shaw.2020.06.005.
G. Shan, S. Xu, L. Yang, S. Jia, and Y. Xiang, "Learn#: A Novel incremental learning method for text classification,” Expert Syst. Appl., vol. 147, p. 113198, 2020, doi: 10.1016/j.eswa.2020.113198.
Y. Liu, C. M. Eckert, and C. Earl, A review of fuzzy AHP methods for decision-making with subjective judgements, vol. 161. Elsevier Ltd, 2020.
H. T. Phan, N. T. Nguyen, V. C. Tran, and D. Hwang, "An approach for a decision-making support system based on measuring the user satisfaction level on Twitter,” Inf. Sci. (Ny)., vol. 561, pp. 243–273, 2021, doi: 10.1016/j.ins.2021.01.008.
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.
H. M. Lyu, W. H. Zhou, S. L. Shen, and A. N. Zhou, "Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen,” Sustain. Cities Soc., vol. 56, p. 102103, 2020, doi: 10.1016/j.scs.2020.102103.
M. T. Al Nahyan, Y. E. Hawas, M. S. Mohammad, and B. Basheerudeen, "A decision-support system for identifying the best contractual delivery methods of mega infrastructure developments,” ICEIS 2018 - Proc. 20th Int. Conf. Enterp. Inf. Syst., vol. 1, no. Iceis 2018, pp. 407–414, 2018, doi: 10.5220/0006694704070414.
D. Liang, Z. Dai, and M. Wang, "Assessing customer satisfaction of O2O takeaway based on online reviews by integrating fuzzy comprehensive evaluation with AHP and probabilistic linguistic term sets,” Appl. Soft Comput. J., no. xxxx, p. 106847, 2020, doi: 10.1016/j.asoc.2020.106847.
B. P. Gowd, K. Jayasree, and M. N. Hegde, "Comparison of Artificial Neural Networks and Fuzzy Logic Approaches for Crack Detection in a Beam Like Structure,” Int. J. Artif. Intell. Appl., vol. 9, no. 1, pp. 35–51, 2018.
A. Setyono and S. N. Aeni, "Development of decision support system for ordering goods using fuzzy Tsukamoto,” Int. J. Electr. Comput. Eng., vol. 8, no. 2, pp. 1182–1193, 2018, doi: 10.11591/ijece.v8i2.pp1182-1193.
A. H. Oluwole, A. A. Adekunle, A. O. Olasunkanmi, and A. O. Adeodu, "A shoveling-related pain intensity prediction expert system for workers' manual movement of material,” Int. J. Technol., vol. 7, no. 4, pp. 603–615, 2016, doi: 10.14716/ijtech.v7i4.2208.
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.
S. A. Sabab, M. A. R. Munshi, A. I. Pritom, and S. Shihabuzzaman, "Cardiovascular disease prognosis using effective classification and feature selection technique,” 1st Int. Conf. Med. Eng. Heal. Informatics Technol. MediTec 2016, no. November, pp. 1–6, 2017, doi: 10.1109/MEDITEC.2016.7835374.
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.
S. L. B. Ginting, J. Adler, Y. R. Ginting, and A. H. Kurniadi, "The Development of Bank Application for Debtors Selection by Using Naí¯ve Bayes Classifier Technique,” IOP Conf. Ser. Mater. Sci. Eng., vol. 407, no. 1, 2018, doi: 10.1088/1757-899X/407/1/012177.
S. H. Alizadeh, A. Hediehloo, and N. Shiri, "Knowledge-Based Systems Multi independent latent component extension of Naí¯ve Bayes classifier,” Knowledge-Based Syst., vol. 213, p. 106646, 2021, doi: 10.1016/j.knosys.2020.106646.
F. Harahap, A. Y. N. Harahap, E. Ekadiansyah, R. N. Sari, R. Adawiyah, and C. B. Harahap, "Implementation of Naí¯ve Bayes Classification Method for Predicting Purchase,” 2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. April, 2019, doi: 10.1109/CITSM.2018.8674324.
M. H. Rifqo and A. Wijaya, "Implementasi Algoritma Naí¯ve Bayes Dalam Penentuan Pemberian Kredit,” Pseudocode, vol. 4, no. 2, pp. 120–128, 2017, doi: 10.33369/pseudocode.4.2.120-128.
Yuliana, Paradise, and Kusrini, "Sistem Pakar Diagnosa Penyakit Ispa Menggunakan Metode Naí¯ve Bayes Classifier Berbasis Web,” CSRID J., vol. 10, no. 3, pp. 127–138, 2018, doi: 10.30998/semnasristek.v6i1.5645.
C. Widiyawati and M. Imron, "Sistem Pakar Diagnosa Penyakit Pada Kucing Menggunakan Metode Naí¯ve Bayes Classifier Expert System Of Cat Disease Diagnosis Using Naí¯ve Bayes Classifier Method,” vol. 17, no. 2, pp. 134–144, 2018.
H. Annur, "Klasifikasi Masyarakat Miskin Menggunakan Metode Naí¯ve Bayes,” Ilk. J. Ilm., vol. 10, no. 2, pp. 160–165, 2018, doi: 10.33096/ilkom.v10i2.303.160-165.
K. NainSukhia, A. Ashraf Khan, and M. Bano, "Introducing Economic Order Quantity Model for Inventory Control in Web based Point of Sale Applications and Comparative Analysis of Techniques for Demand Forecasting in Inventory Management,” Int. J. Comput. Appl., vol. 107, no. 19, pp. 1–8, 2014, doi: 10.5120/18856-7385.
Copyright (c) 2022 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
