Practical Use of the Subjective Mathematical Model of Bayes and Its External Validation in Dental Medicine & Dentistry
- 1 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 2 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 3 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 4 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 5 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 6 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 7 Department of Prosthodontics, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 8 Department of Prosthodontics, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 9 Department of Periodontology, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 10 Department of Mathematics-Computer Science & Statistics, Faculty of Sciences (MRL and Bayes), University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 11 Department of Public Health, Statistics, Management, Faculty of Sciences (MRL and Bayes), University of Kinshasa, Kinshasa, Democratic Republic of Congo
- 12 The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- 13 Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
- 14 Department of Oral Maxillofacial Head and Neck Oncology Surgery, School and Hospital of Stomatology, Wuhan University, Wuhan, China
Abstract
Objective: Our study aims to validate the subjective Bayes mathematical model using the mathematical model of logistic regression. Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning in Dentistry. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential. Method: The study used the Subjective Mathematical Bayes Model (SBM) approach and employed Logistic Regression Mathematical Model (LMR) techniques. The external validation of the subjective mathematical Bayes model (MSB) concerns the real cases of 625 patients who developed alveolar osteitis (OA). We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis Logistic Regression Mathematical Model (LMR) techniques and on artificial intelligence, a major goal in the next decade. Results: The internal validation had identified seven (7) etiological factors of OA, which will be compared to the cases of MRL, for the external validation which retained six (6) etiological factors of OA. The experts in the internal validation of the MSB had generated 40 cases of OA and a COP of (0.5), which will be compared to the MRL that collected 625 real cases of OA to produce a Cop of (0.6) in the external validation, which discriminates between healthy patients (Se) and sick patients (Sp). Compared to real cases and the logistic regression model, the Bayesian model is efficient and its validity is established.
- Muyembi, M.P., Sekele, J.P., Kashiama, B., Bobe, P., Kalala, E.M., Nyimi Bushabu, F., et al . (2016) Internal Validation of Subjective Bayesian Model of the Occurrence of Alveolar Osteitis Cases, Generated by Expert in Kinshasa Hospital/DRC. Journal of Orthodontics & Endodontics , 2, 1-9. https://doi.org/10.21767/2469-2980.100026
- Barbosa, N.L., Thome, A., Maciel, C.C., Oliveria, J., et al . (2011) Facteurs associés aux complications de la suppression des troisièmes molaires: Une étude transversale. Medicina Oral , Patologia Oral , Cirugia Bucal , 16, 376-380.
- Ortega, R., Loria, A. and Kelly, R. (1995) A Semiglobally Stable Output Feedback PI2D Regulator for Robot Manipulators. IEEE Transactions on Automatic Control , 40, 1432-1436. https://doi.org/10.1109/9.402235
- Wu, J.K. (1994) Two Problems of Computer Mechanics Program System. In: Proceedings of Finite Element Analysis and CAD , Peking University Press, Beijing, 9-15.
- Pewsner, D., Bleuer, J., Bucher, H., Battaglia, M., Jüni, P. and Egger, M. (2001) Sur la voie de l’intuition? Théorème de Bayes et diagnostic en médecine générale. Partie I. Forum Médical Suisse , 3, 41-45. https://doi.org/10.4414/fms.2001.04011
- Apaza, T., Mfiengui, N. and Ntumba, M.K. (1993) Alvéolites post-extractionnelle. A propos de 140 cas observés au Centre médico-chirurgical du Camp Kokolo à Kinshasa (RDC). Panorama Médical , 1, 431-435.
- Grolier, J., Heresbach, D. and Josselin, J.-M. (2009) Le calcul appliqué. L’analyse coût-efficacité au service de la décision en santé publique. Presses de l‘EHESP. https://www.presses.ehesp.fr/
- Bwira Mwokozi, E., Kamwina, K., Ngoie Bangwa, R. and Munyanga Mukongo, S. (2015) Analyse prédictive des facteurs de la survenue de l’accident vasculaire cérébrale chez les professeurs de l’Université de Kinshasa, en utilisant le modèle bayésien. Ann ale de la faculté des sciences , 1, 31-41.
- Press, S.J. (2003) Subjective and Objective Bayesian Statistics: Principles, Models, and Applications. 2nd Edition, Wiley.
- Soureshjani, M.H. and Kimiagari, A. (2013) Calculating the Best Cut off Point Using Logistic Regression and Neural Network on Credit Scoring Problem—A Case Study of a Commercial Bank. African Journal of Business Management , 7, 1414-1421.
- Roche, Y. and Gogly, B. (1990) Etiopathogénie de l’alvéolite sèche: Données actuelles. Actual Odontostomatol , 170, 323-324.
- Cissokho, C.A. (1998) Le point sur les alvéolites et leur prise en charge thérapeutique: Etude prospective à propos de 68cas. Thèse en chirurgie dentaire. Dakar.