scholarly journals Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

2021 ◽  
Vol 9 (12) ◽  
pp. 141
Author(s):  
Francisco Ramos-Gomez ◽  
Marvin Marcus ◽  
Carl A. Maida ◽  
Yan Wang ◽  
Janni J. Kinsler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oral Health ◽  
Los Angeles ◽  
Dental Caries ◽  
Health Problem ◽  
Learning Algorithm ◽  
Survey Methods ◽  
Public Health Problem ◽  
Caries Experience ◽  
Machine Learning Algorithm

Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.

scholarly journals Dental caries experience among school children of Eastern Nepal - significant caries index

2015 ◽  
Vol 12 (2) ◽  
pp. 74-77 ◽  
Author(s):  
T K Bhagat ◽  
A Shrestha
Keyword(s):  
Public Health ◽  
Dental Caries ◽  
School Children ◽  
Health Problem ◽  
Public Health Problem ◽  
Caries Experience ◽  
Who Criteria ◽  
Dental Hard Tissues ◽  
Hard Tissues ◽  
The Mean

Background: Dental caries, an infectious microbiologic disease of dental hard tissues, is a common public health problem worldwide. The distribution of dental caries studied in any population, shows that a few in the population experience a lot of decayed teeth and most do not experience any at all or experience very little.Objective: To assess the extent and severity of dental caries among 5-12 years old children of eastern Nepal using DMFT(Decayed, Missing and Filled teeth) and SiC (Significant Caries) index.Methods: Six hundred and sixteen 5-12 years old school children were examined for decayed, missing and filled teeth using WHO criteria. DMFT, dft (decayed filled teeth) and SiC Indices were calculated. Results: The mean dft and SiC* were 1.84 and 4.60 respectively, whereas mean DMFT and SiC were 0.33 and 0.92 respectively.Conclusion: SiC gives a better picture of the at risk population, hence it should be widely used along with dft/DMFT.Health Renaissance 2014;12(2): pp 74-77

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Does non-institutionalized elders have a better oral health status compared to institutionalized ones? A systematic review and meta-analysis

2020 ◽  
Vol 25 (6) ◽  
pp. 2177-2192 ◽  
Author(s):  
Ilky Pollansky Silva e Farias ◽  
Simone Alves de Sousa ◽  
Leopoldina de Fátima Dantas de Almeida ◽  
Bianca Marques Santiago ◽  
Antonio Carlos Pereira ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Status ◽  
Oral Health ◽  
Dental Caries ◽  
Meta Analysis ◽  
Oral Health Status ◽  
Cochrane Library ◽  
Caries Experience ◽  
Institutionalized Elderly ◽  
Periodontal Status

Abstract This systematic review compared the oral health status between institutionalized and non-institutionalized elders. The following electronic databases were searched: PubMed (Medline), Scopus, Web of Science, Lilacs and Cochrane Library, in a comprehensive and unrestricted manner. Electronic searches retrieved 1687 articles, which were analyzed with regards to respective eligibility criteria. After reading titles and abstracts, five studies were included and analyzed with respect their methodological quality. Oral status of institutionalized and non-institutionalized elderly was compared through meta-analysis. Included articles involved a cross-sectional design, which investigated 1936 individuals aged 60 years and over, being 999 Institutionalized and 937 non-institutionalized elders. Studies have investigated the prevalence of edentulous individuals, the dental caries experience and the periodontal status. Meta-analysis revealed that institutionalized elderly have greater prevalence of edentulous (OR = 2.28, 95%CI = 1.68-3.07) and higher number of decayed teeth (MD = 0.88, 95%CI = 0.71-1.05) and missed teeth (MD = 4.58, 95%CI = 1.89-7.27). Poor periodontal status did not differ significantly between groups. Compared to non-institutionalized, institutionalized elders have worse dental caries experience.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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