A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors

Dental caries is a multifactorial disease that can be caused by interactions between genetic and environmental risk factors. Despite the availability of caries risk assessment tools, caries risk prediction models incorporating new factors, such as human genetic markers, have not yet been reported. The aim of this study was to construct a new model for caries risk prediction in teenagers, based on environmental and genetic factors, using a machine learning algorithm. We performed a prospective longitudinal study of 1,055 teenagers (710 teenagers for cohort 1 and 345 teenagers for cohort 2) aged 13 years, of whom 953 (633 teenagers for cohort 1 and 320 teenagers for cohort 2) were followed for 21 months. All participants completed an oral health questionnaire, an oral examination, biological (salivary and cariostate) tests, and single nucleotide polymorphism sequencing analysis. We constructed a caries risk prediction model based on these data using a random forest with an AUC of 0.78 in cohort 1 (training cohort). We further verified the discrimination and calibration abilities of this caries risk prediction model using cohort 2. The AUC of the caries risk prediction model in cohort 2 (testing cohort) was 0.73, indicating high discrimination ability. Risk stratification revealed that our caries risk prediction model could accurately identify individuals at high and very high caries risk but underestimated risks for individuals at low and very low caries risk. Thus, our caries risk prediction model has the potential for use as a powerful community-level tool to identify individuals at high caries risk.

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Abstract PR-11: Developing an agnostic risk prediction model for acute kidney injury in cancer patients using a machine learning algorithm from blood results data

10.1158/1557-3265.adi21-pr-11 ◽

2021 ◽

Author(s):

Lauren A. Scanlon ◽

Jorge Barrusio ◽

Alexander Garbett

Keyword(s):

Machine Learning ◽

Acute Kidney Injury ◽

Prediction Model ◽

Cancer Patients ◽

Risk Prediction ◽

Learning Algorithm ◽

Kidney Injury ◽

Risk Prediction Model ◽

Machine Learning Algorithm

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Machine Learning Algorithm Credit Risk Prediction Model

Advances in Intelligent Systems and Computing - 2020 International Conference on Applications and Techniques in Cyber Intelligence ◽

10.1007/978-3-030-53980-1_15 ◽

2020 ◽

pp. 93-99

Author(s):

Liping Wang ◽

Fanglin An

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Credit Risk ◽

Risk Prediction ◽

Learning Algorithm ◽

Risk Prediction Model ◽

Machine Learning Algorithm

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Machine learning algorithm-based risk prediction model of coronary artery disease

Molecular Biology Reports ◽

10.1007/s11033-018-4236-2 ◽

2018 ◽

Vol 45 (5) ◽

pp. 901-910 ◽

Cited By ~ 5

Author(s):

Shaik Mohammad Naushad ◽

Tajamul Hussain ◽

Bobbala Indumathi ◽

Khatoon Samreen ◽

Salman A. Alrokayan ◽

...

Keyword(s):

Machine Learning ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Prediction Model ◽

Risk Prediction ◽

Learning Algorithm ◽

Risk Prediction Model ◽

Machine Learning Algorithm ◽

Artery Disease

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Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2021.104429 ◽

2021 ◽

Vol 149 ◽

pp. 104429

Author(s):

Jun Li ◽

Qingguang Chen ◽

Xiaojuan Hu ◽

Pei Yuan ◽

Longtao Cui ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Risk Prediction ◽

Diabetes Risk ◽

Risk Prediction Model ◽

Machine Learning Techniques ◽

Model Based ◽

Learning Techniques

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Computer System Integration Technology of Hydraulic Engineering Design Based on Machine Learning Algorithm

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/3/032058 ◽

2021 ◽

Vol 2083 (3) ◽

pp. 032058

Author(s):

Ting Liu

Keyword(s):

Machine Learning ◽

Engineering Design ◽

Computer System ◽

Risk Prediction ◽

System Integration ◽

Learning Algorithm ◽

Hydraulic Engineering ◽

Machine Learning Algorithm ◽

Water Conservancy ◽

Learning Machine

Abstract With the development of water conservancy informatization, the research on water information system integration is born, which is the need of water conservancy informatization construction at present and also an urgent problem to be solved. Based on the machine learning algorithm, combined with the actual needs of water conservancy business field, the overall framework of computer system integration for water conservancy engineering design is put forward. The overall framework includes: resource layer, comprehensive integration layer and user layer, which exchange data with configuration monitoring software by means of communication. The analytic hierarchy process in machine learning algorithm is used to construct the risk prediction index system, and the risk prediction index and initial prediction results are taken as the input and output of extreme learning machine algorithm in machine learning algorithm. The simulation results show that the prediction accuracy of this method is 94.88%, which can accurately predict the risks existing in hydraulic engineering design computer system and improve the system security.

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A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data

BMC Medical Genomics ◽

10.1186/s12920-019-0607-3 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Daichi Shigemizu ◽

Shintaro Akiyama ◽

Yuya Asanomi ◽

Keith A. Boroevich ◽

Alok Sharma ◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Prediction Model ◽

Risk Prediction ◽

Mirna Expression ◽

Dementia With Lewy Bodies ◽

Target Genes ◽

Lewy Bodies ◽

Risk Prediction Model ◽

Gradient Boosting

Abstract Background Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. Methods In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. Results The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Conclusions Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.

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