Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis

Cardiometabolic diseases, developed throughout the worker’s life,such as hypertension, diabetes, dyslipidemia and obesity are amongthe main causes of death and are associated with modifiable andcontrollable risk factors. The general objective of this study wasto apply supervised Machine Learning techniques and to comparetheir performance to predict the risk of developing cardiometabolicdisease from servers working at the School Hospital of south inBrazil. We sought to map the characteristics of individuals who aremore likely to develop cardiometabolic diseases. The machine learningmodels evaluated were Naive Bayes, Decision Tree, RandomForest, KNN, Logistic Regression and SVM. The results obtained inthe experiments showed that some supervised machine learningmodels produce a good classification, depending on the attributesand hyperparameters used.

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FRI0046 PHARMACOGENOMICS-DRIVEN INDIVIDUALIZED PREDICTION OF TREATMENT RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.4993 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 598.2-598

Author(s):

E. Myasoedova ◽

A. Athreya ◽

C. S. Crowson ◽

R. Weinshilboum ◽

L. Wang ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Machine Learning ◽

Supervised Machine Learning ◽

Research Support ◽

Eular Response ◽

Learning Methods ◽

Machine Learning Methods ◽

Early Ra ◽

Genome Wide

Background:Methotrexate (MTX) is the most common anchor drug for rheumatoid arthritis (RA), but the risk of missing the opportunity for early effective treatment with alternative medications is substantial given the delayed onset of MTX action and 30-40% inadequate response rate. There is a compelling need to accurately predicting MTX response prior to treatment initiation, which allows for effectively identifying patients at RA onset who are likely to respond to MTX.Objectives:To test the ability of machine learning approaches with clinical and genomic biomarkers to predict MTX response with replications in independent samples.Methods:Age, sex, clinical, serological and genome-wide association study (GWAS) data on patients with early RA of European ancestry from 647 patients (336 recruited in United Kingdom [UK]; 307 recruited across Europe; 70% female; 72% rheumatoid factor [RF] positive; mean age 54 years; mean baseline Disease Activity Score with 28-joint count [DAS28] 5.65) of the PhArmacogenetics of Methotrexate in RA (PAMERA) consortium was used in this study. The genomics data comprised 160 genome-wide significant single nucleotide polymorphisms (SNPs) with p<1×10-5 associated with risk of RA and MTX metabolism. DAS28 score was available at baseline and 3-month follow-up visit. Response to MTX monotherapy at the dose of ≥15 mg/week was defined as good or moderate by the EULAR response criteria at 3 months’ follow up visit. Supervised machine-learning methods were trained with 5-repeats and 10-fold cross-validation using data from PAMERA’s 336 UK patients. Class imbalance (higher % of MTX responders) in training was accounted by using simulated minority oversampling technique. Prediction performance was validated in PAMERA’s 307 European patients (not used in training).Results:Age, sex, RF positivity and baseline DAS28 data predicted MTX response with 58% accuracy of UK and European patients (p = 0.7). However, supervised machine-learning methods that combined demographics, RF positivity, baseline DAS28 and genomic SNPs predicted EULAR response at 3 months with area under the receiver operating curve (AUC) of 0.83 (p = 0.051) in UK patients, and achieved prediction accuracies (fraction of correctly predicted outcomes) of 76.2% (p = 0.054) in the European patients, with sensitivity of 72% and specificity of 77%. The addition of genomic data improved the predictive accuracies of MTX response by 19% and achieved cross-site replication. Baseline DAS28 scores and following SNPs rs12446816, rs13385025, rs113798271, and rs2372536 were among the top predictors of MTX response.Conclusion:Pharmacogenomic biomarkers combined with DAS28 scores predicted MTX response in patients with early RA more reliably than using demographics and DAS28 scores alone. Using pharmacogenomics biomarkers for identification of MTX responders at early stages of RA may help to guide effective RA treatment choices, including timely escalation of RA therapies. Further studies on personalized prediction of response to MTX and other anti-rheumatic treatments are warranted to optimize control of RA disease and improve outcomes in patients with RA.Disclosure of Interests:Elena Myasoedova: None declared, Arjun Athreya: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Richard Weinshilboum Shareholder of: co-founder and stockholder in OneOme, Liewei Wang: None declared, Eric Matteson Grant/research support from: Pfizer, Consultant of: Boehringer Ingelheim, Gilead, TympoBio, Arena Pharmaceuticals, Speakers bureau: Simply Speaking

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A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

Psychological Medicine ◽

10.1017/s003329172100489x ◽

2021 ◽

pp. 1-10

Author(s):

I. Krug ◽

J. Linardon ◽

C. Greenwood ◽

G. Youssef ◽

J. Treasure ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Predictive Accuracy ◽

Area Under The Curve ◽

Prediction Rule ◽

Predictive Performance ◽

Individual Risk ◽

European Project ◽

Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

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Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease

Frontiers in Health Informatics ◽

10.30699/fhi.v10i1.349 ◽

2021 ◽

Vol 10 (1) ◽

pp. 99

Author(s):

Sajad Yousefi

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Heart Disease ◽

Decision Tree ◽

Roc Curve ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Learning Models ◽

Algorithm Performance ◽

Machine Learning Models

Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.

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Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method

Frontiers in Public Health ◽

10.3389/fpubh.2021.619429 ◽

2021 ◽

Vol 9 ◽

Author(s):

Huanhuan Zhao ◽

Xiaoyu Zhang ◽

Yang Xu ◽

Lisheng Gao ◽

Zuchang Ma ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Risk Prediction ◽

Disease Risk ◽

Learning Algorithms ◽

Large Population ◽

Machine Learning Algorithms ◽

Hypertension Risk ◽

Model Training

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.

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A Statistical Analysis of Risk Factors and Biological Behavior in Canine Mammary Tumors: A Multicenter Study

Animals ◽

10.3390/ani10091687 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1687

Author(s):

Giovanni P. Burrai ◽

Andrea Gabrieli ◽

Valentina Moccia ◽

Valentina Zappulli ◽

Ilaria Porcellato ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Statistical Analysis ◽

Multicenter Study ◽

Malignant Tumors ◽

Mammary Tumors ◽

Supervised Machine Learning ◽

Biological Behavior ◽

Clinical Staging ◽

Canine Mammary Tumors

Canine mammary tumors (CMTs) represent a serious issue in worldwide veterinary practice and several risk factors are variably implicated in the biology of CMTs. The present study examines the relationship between risk factors and histological diagnosis of a large CMT dataset from three academic institutions by classical statistical analysis and supervised machine learning methods. Epidemiological, clinical, and histopathological data of 1866 CMTs were included. Dogs with malignant tumors were significantly older than dogs with benign tumors (9.6 versus 8.7 years, p < 0.001). Malignant tumors were significantly larger than benign counterparts (2.69 versus 1.7 cm, p < 0.001). Interestingly, 18% of malignant tumors were smaller than 1 cm in diameter, providing compelling evidence that the size of the tumor should be reconsidered during the assessment of the TNM-WHO clinical staging. The application of the logistic regression and the machine learning model identified the age and the tumor’s size as the best predictors with an overall diagnostic accuracy of 0.63, suggesting that these risk factors are sufficient but not exhaustive indicators of the malignancy of CMTs. This multicenter study increases the general knowledge of the main epidemiologica-clinical risk factors involved in the onset of CMTs and paves the way for further investigations of these factors in association with CMTs and in the application of machine learning technology.

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Risk Factors for Post-Transplant Outcomes in Patients with LVAD Support: A Machine Learning and Logistic Regression of the UNOS Database

The Journal of Heart and Lung Transplantation ◽

10.1016/j.healun.2020.01.169 ◽

2020 ◽

Vol 39 (4) ◽

pp. S410

Author(s):

C.A. Bravo ◽

M. Alvarez Villela ◽

M. Shah ◽

R. Merekar ◽

P. Oliva Mella ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Post Transplant ◽

Transplant Outcomes ◽

Lvad Support

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Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01268-x ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Divneet Mandair ◽

Premanand Tiwari ◽

Steven Simon ◽

Kathryn L. Colborn ◽

Michael A. Rosenberg

Keyword(s):

Neural Network ◽

Machine Learning ◽

Risk Factors ◽

Myocardial Infarction ◽

Logistic Regression ◽

Large Scale ◽

Deep Neural Network ◽

Low Frequency ◽

Common Data Model ◽

Electronic Health Record Data

Abstract Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.

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Machine learning methods for diagnosis of chronic obstructive pulmonary disease in healthy subjects: Analysis of Risk factors To DEtect COPD (ARTDECO) (Preprint)

10.2196/preprints.24796 ◽

2020 ◽

Author(s):

Shigeo Muro ◽

Masato Ishida ◽

Yoshiharu Horie ◽

Wataru Takeuchi ◽

Shunki Nakagawa ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Chronic Obstructive Pulmonary Disease ◽

Logistic Regression ◽

Pulmonary Disease ◽

Vital Capacity ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Copd Diagnosis

BACKGROUND Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances including tobacco smoke is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. OBJECTIVE To predict risk factors for COPD diagnosis using machine learning in an annual medical check-up database. METHODS In this retrospective, observational cohort study (Analysis of Risk factors To DEtect COPD [ARTDECO]), annual medical check-up records for all Hitachi Ltd. employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30–75 years, without prior diagnosis of COPD, asthma, or history of cancer were included. The database included clinical measurements (e.g., pulmonary function tests) and questionnaire responses. To predict risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning method (XGBoost) was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the pre-bronchodilator forced expiratory volume in 1 second (FEV1) to pre-bronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations. RESULTS Of the 26,101 individuals screened, 1,213 met the exclusion criteria and thus 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity value, and percent predicted value of FEV1. The area under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively. CONCLUSIONS Using a machine learning model in this longitudinal database, we identified a set multiple of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan. CLINICALTRIAL Not applicable.

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Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018

Frontiers in Nutrition ◽

10.3389/fnut.2021.669155 ◽

2021 ◽

Vol 8 ◽

Author(s):

Sri Astuti Thamrin ◽

Dian Sidik Arsyad ◽

Hedi Kuswanto ◽

Armin Lawi ◽

Sudirman Nasir

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Health Research ◽

Health Data ◽

Basic Health ◽

Multiple Risk Factors ◽

Obesity Status ◽

Address Data ◽

Multiple Risk

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

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