Author(s)

Muyembi Muinaminayi Pierre¹, Kayembe Mwimbi David^1,2*, Nyimi Boshabu Fidèle^1,3, Panoumvita Kapamona Junior¹, Nsudila Mpoyi Monique¹, Tubanza Mulongo Simplot¹, Sekele Issouradi-Bourley Jean-Paul⁴, Mantshumba Milolo Augustin⁴, Kalala Kazadi Em⁵, Mabela Rosti⁶, Munyanga Mukongo Sylvain⁷, Dan Wang⁸

¹Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo.
²Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
³Department of Oral Maxillofacial Head and Neck Oncology Surgery, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
⁴Department of Prosthodontics, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo.
⁵Department of Periodontology, Faculty of Dental Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo.
⁶Department of Mathematics-Computer Science & Statistics, Faculty of Sciences (MRL and Bayes), University of Kinshasa, Kinshasa, Democratic Republic of Congo.
⁷Department of Public Health, Statistics, Management, Faculty of Sciences (MRL and Bayes), University of Kinshasa, Kinshasa, Democratic Republic of Congo.
⁸The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.

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.

External Validation-MSB-MRL, Mathematical Model

Pierre, M. , David, K. , Fidèle, N. , Junior, P. , Monique, N. , Simplot, T. , Jean-Paul, S. , Augustin, M. , Em, K. , Rosti, M. , Sylvain, M. and Wang, D. (2024) Practical Use of the Subjective Mathematical Model of Bayes and Its External Validation in Dental Medicine & Dentistry. Open Journal of Statistics, 14, 553-575. doi: 10.4236/ojs.2024.145025.