Analysis of Risk Factors for Cervical Cancer Based on Machine Learning Methods

Introduction: Applications of machine learning (ML) methods have been demonstrated by the recent FDA approval of new ML-based biomedical image processing methods. In this study, we examine applications of ML, specifically artificial neural networks (ANN), for predicting risk of cardiovascular (CV) events. Hypothesis: We hypothesized that using the same CV risk factors, ML-based CV prediction models can improve the performance of current predictive models. Methods: Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER; NCT00239681) is a multi-ethnic trial that randomized non-diabetic participants with LDL-C<130 mg/dL and hsCRP≥2 mg/L to rosuvastatin versus placebo. We restricted the analysis to white and black participants allocated to the placebo arm, and estimated the race- and sex-specific Pooled Cohorts Equations (PCE) 5-year risk score using race, sex, age, HDL-C, total cholesterol, systolic BP, antihypertensive medications, and smoking. A total of 218 incident CV cases occurred (maximum follow-up 5 years). For every participant in the case group, we randomly selected 4 controls from the placebo arm after stratifying for the baseline risk factors (Table 1). The risk factors from a total of n=1,090 participants were used to train and test the ANN model. We used 80% of the participants (n=872) for designing the network and left out 20% of the data (n=218) for testing the predictive model. We used the TensorFlow software to design, train, and evaluate the ANN model. Results: We compared the performances of the ANN and the PCE score on the 218 test subjects (Figure 1). The high AUC of the neural network (0.85; 95% CI 0.78-0.91) on this dataset suggests advantages of machine learning methods compared to the current methods. Conclusions: This result demonstrates the potential of machine learning methods for enhancing and improving the current techniques used in cardiovascular risk prediction and should be evaluated in other cohorts.

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Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM)

European Psychiatry ◽

10.1016/j.eurpsy.2016.06.003 ◽

2017 ◽

Vol 39 ◽

pp. 40-50 ◽

Cited By ~ 13

Author(s):

J.F. Dipnall ◽

J.A. Pasco ◽

M. Berk ◽

L.J. Williams ◽

S. Dodd ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Heterogeneous Data ◽

Multivariate Logistic Regression ◽

Learning Methods ◽

Machine Learning Methods ◽

Health And Nutrition ◽

Unhealthy Eating ◽

Health Factors ◽

Mental Health Factors

AbstractBackgroundKey lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through “Graphing lifestyle-environs using machine-learning methods” (GLUMM).MethodsTwo ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six “lifestyle-environ” variables were used from the National health and nutrition examination study (2009–2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders.ResultsThe SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤ 2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤ 14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P < 0.001) and GLUMM7-1 (OR: 7.88, P < 0.001) with depression was found, with significant interactions with those married/living with partner (P = 0.001).ConclusionUsing ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors.

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Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

Expert Systems ◽

10.1111/exsy.12413 ◽

2019 ◽

Vol 36 (4) ◽

Author(s):

Syed Asif Raza ◽

Lukman Thalib ◽

Jassim Al Suwaidi ◽

Kadhim Sulaiman ◽

Wael Almahmeed ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Acute Coronary Syndrome ◽

Mortality Risk ◽

Arabian Gulf ◽

Learning Methods ◽

Coronary Syndrome ◽

Machine Learning Methods

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Identifying Significant Injury Severity Risk Factors in Traffic Accidents Based on the Machine Learning Methods

CICTP 2019 ◽

10.1061/9780784482292.326 ◽

2019 ◽

Author(s):

Wei Zhang ◽

Zhuping Zhou ◽

Lei Li ◽

Rui Huang

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Traffic Accidents ◽

Injury Severity ◽

Learning Methods ◽

Machine Learning Methods ◽

Significant Injury

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Post-stroke Anxiety Analysis via Machine Learning Methods

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.657937 ◽

2021 ◽

Vol 13 ◽

Author(s):

Jirui Wang ◽

Defeng Zhao ◽

Meiqing Lin ◽

Xinyu Huang ◽

Xiuli Shang

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Ischemic Stroke ◽

Multivariate Logistic Regression ◽

Learning Methods ◽

Anxiety Group ◽

Post Stroke ◽

Machine Learning Methods

Post-stroke anxiety (PSA) has caused wide public concern in recent years, and the study on risk factors analysis and prediction is still an open issue. With the deepening of the research, machine learning has been widely applied to various scenarios and make great achievements increasingly, which brings new approaches to this field. In this paper, 395 patients with acute ischemic stroke are collected and evaluated by anxiety scales (i.e., HADS-A, HAMA, and SAS), hence the patients are divided into anxiety group and non-anxiety group. Afterward, the results of demographic data and general laboratory examination between the two groups are compared to identify the risk factors with statistical differences accordingly. Then the factors with statistical differences are incorporated into a multivariate logistic regression to obtain risk factors and protective factors of PSA. Statistical analysis shows great differences in gender, age, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level between PSA group and non-anxiety group with HADS-A and HAMA evaluation. Meanwhile, as evaluated by SAS scale, gender, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level differ in the PSA group and the non-anxiety group. Multivariate logistic regression analysis of HADS-A, HAMA, and SAS scales suggest that hypertension, diabetes mellitus, drinking, high NIHSS score, and low serum HDL-C level are related to PSA. In other words, gender, age, disability, hypertension, diabetes mellitus, HDL-C, and drinking are closely related to anxiety during the acute stage of ischemic stroke. Hypertension, diabetes mellitus, drinking, and disability increased the risk of PSA, and higher serum HDL-C level decreased the risk of PSA. Several machine learning methods are employed to predict PSA according to HADS-A, HAMA, and SAS scores, respectively. The experimental results indicate that random forest outperforms the competitive methods in PSA prediction, which contributes to early intervention for clinical treatment.

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High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease: metabolic and clinical predictors identified by machine learning

BMJ Open Gastroenterology ◽

10.1136/bmjgast-2020-000532 ◽

2020 ◽

Vol 7 (1) ◽

pp. e000532

Author(s):

Martin McDonnell ◽

Richard J Harris ◽

Florina Borca ◽

Tilly Mills ◽

Louise Downey ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Clinical Activity ◽

Published Data ◽

Clinical Predictors ◽

Learning Methods ◽

Machine Learning Methods ◽

Inflammatory Bowel

BackgroundGlucocorticosteroids (GC) are long-established, widely used agents for induction of remission in inflammatory bowel disease (IBD). Hyperglycaemia is a known complication of GC treatment with implications for morbidity and mortality. Published data on prevalence and risk factors for GC-induced hyperglycaemia in the IBD population are limited. We prospectively characterise this complication in our cohort, employing machine-learning methods to identify key predictors of risk.MethodsWe conducted a prospective observational study of IBD patients receiving intravenous hydrocortisone (IVH). Electronically triggered three times daily capillary blood glucose (CBG) monitoring was recorded alongside diabetes mellitus (DM) history, IBD biomarkers, nutritional and IBD clinical activity scores. Hyperglycaemia was defined as CBG ≥11.1 mmol/L and undiagnosed DM as glycated haemoglobin ≥48 mmol/mol. Random forest (RF) regression models were used to extract predictor-patterns present within the dataset.Results94 consecutive IBD patients treated with IVH were included. 60% (56/94) of the cohort recorded an episode of hyperglycaemia, including 57% (50/88) of those with no history of DM, of which 19% (17/88) and 5% (4/88) recorded a CBG ≥14 mmol/L and ≥20 mmol/L, respectively. The RF models identified increased C-reactive protein (CRP) followed by a longer IBD duration as leading risk predictors for significant hyperglycaemia.ConclusionHyperglycaemia is common in IBD patients treated with intravenous GC. Therefore, CBG monitoring should be included in routine clinical practice. Machine learning methods can identify key risk factors for clinical complications. Steroid-sparing treatment strategies may be considered for those IBD patients with higher admission CRP and greater disease duration, who appear to be at the greatest risk of hyperglycaemia.

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USING MACHINE LEARNING METHODS FOR THE CORRECTION OF BEHAVIORAL RISK FACTORS FOR PREVENTION OF CARDIOVASCULAR DISEASES

Cherepovets State University Bulletin ◽

10.23859/1994-0637-2020-4-97-5 ◽

2020 ◽

Vol 4 (97) ◽

pp. 54-68

Author(s):

GEORGII G. RAPAKOV ◽

GENNADII T. BANSHCHIKOV ◽

VYACHESLAV A. GORBUNOV ◽

ALEKSEY V. UDARATIN

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Cardiovascular Diseases ◽

Arterial Hypertension ◽

Behavioral Risk Factors ◽

Behavioral Risk ◽

Confidence Estimation ◽

Learning Methods ◽

Machine Learning Methods ◽

Regional System

The article describes machine learning methods in the correction of behavioral risk factors while preventing cardiovascular diseases. The monitoring of health saving educational space in the regional system of medical prevention was implemented. Applying computer modeling the authors developed a model of binding rules based on the method of association rules and suggested the set of 5 logical rules for the risk factor of arterial hypertension. Decision tree method was used to induce decision rules and identify the target group to correct risk factors and increase the quality of arterial hypertension control. The present study provided the analysis and confidence estimation of the prognostic model. The results of this analysis were used to support management decisions in the regional system of preventive medicine.

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