Moringa oleifera is a considerable ethnomedical herb with various bioactive compounds. This study aimed to analyze the efficacy of M. oleifera in the prevention and management of various oral conditions. A thorough search was conducted on the Web of Science, Scopus, PubMed, and PubMed Central databases. After screening the data on the basis of inclusion and exclusion criteria, 9 studies were considered for further meta-analysis. The analysis was performed on R programming software (version R-4.0.2) and the results were represented by a forest plot. The estimate obtained via common and random effects model for in vitro studies was statistically insignificant (I2 test P > 0.05) with risk ratios of 8.25 (95% CI: 3.76-18.08) and 7.98 (95% CI: 3.64-17.50) and for in vivo studies were statistically significant (I2 test P )0.05 < with risk ratios of 1.12 (95% CI: 0.90-1.40) and 0.97 (95% CI: 0.71-1.32), indicating the efficacy of M. oleifera in oral diseases on animal and clinical trials, whereas it failed to report the efficacy on in vitro level. Future research has to be done to come up with new and more phytoactive compounds from all parts of the plant with proper extraction procedures. The effectiveness of the compounds has to be validated first on in vitro scale followed by clinical trials so that M. oleifera can be used as therapy in preventing and managing oral ailments.

Introduction: Recent developments in data science and the employment of machine learning algorithms (ML) have revolutionized health sciences in the prediction of diseases using laboratory data. Oral diseases are observed in all age groups and are estimated to affect about a 3.5billion people as per WHO 2022 statistics. Using the existing diagnostic data and taking advantage of ML and prediction models would benefit developing a prediction model for diagnosing oral diseases. Hence, it is quite essential to understand the basic terminologies used in the prediction model. Methods: We retrieve various research papers using Scopus, PubMed, and google scholar databases, where prediction models were used in dentistry. The idea of this review is to explore current models, model validation, discrimination, calibration, and bootstrapping methods used in prediction models for oral diseases. Results: The current advancement of ML techniques plays a significant task in the diagnosis and prognosis of oral diseases. Conclusion: The use of prediction models using ML techniques can improve the accuracy of the treatment methods in oral health. This article aims to provide the required framework, data sets, and methodology to build ML and prediction models for oral diseases.

The huge preponderance of inferences drawn in empirical medical research follows from model-based relations (e.g. regression). Here, we described the role of predictive modeling as a complement to this approach. Predictive models are usually probabilistic model which gives a good quality fit to our data. In medical research, it’s very common to use regression models for predictive purposes. Here in this article, we described the types of predictive modeling (Linear and Non-linear) used in medical research and how effectively the researchers take decisions based on predictive modeling, and what precautions, we have to take while building a predictive model. Finally, we consider a working example to illustrate the effectiveness of the predictive model in healthcare.

In healthcare research, predictive modeling is commonly utilized to forecast risk variables and enhance treatment procedures for improved patient outcomes. Enormous quantities of data are being created as a result of recent advances in research, clinical trials, next-generation genomic sequencing, biomarkers, and transcriptional and translational studies. Understanding how to handle and comprehend scientific data to offer better treatment for patients is critical. Currently, multiple prediction models are being utilized to investigate patient outcomes. However, it is critical to recognize the limitations of these models in the research design and their unique benefits and drawbacks. In this overview, we will look at linear regression, logistic regression, decision trees, and artificial neural network prediction models, as well as their advantages and disadvantages. The two most perilous requirements for building any predictive healthcare model are feature selection and model validation. Typically, feature selection is done by a review of the literature and expert opinion on that subject. Model validation is also an essential component of every prediction model. It characteristically relates to the predictive model's performance and accuracy. It is strongly recommended that all clinical parameters should be thoroughly examined before using any prediction model.