Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors

Background: Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional linear models. Objective: To improve upon the Revised-Framingham Stroke Risk Score and design an interactive non-linear Stroke Risk Score (NSRS). Our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable user-friendly fashion. Methods: A two phase approach was used to develop our stroke risk score predictor. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model consisting of 14,196 samples where each clinical examination was considered an independent observation. Optimal Classification Trees (OCT) were used to train a model to predict 10-year stroke risk. Second, this model was validated with 17,527 observations from the Boston Medical Center. The NSRS was developed into an online user friendly application in the form of a questionnaire (http://www.mit.edu/~agniorf/files/questionnaire_Cohort2.html). Results: The algorithm suggests a key dichotomy between patients with or without history of cardiovascular disease. While the model agrees with known findings, it also identified 23 unique stroke risk profiles and introduced new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient’s risk profile. Our results in both the training and validation populations suggested that the non-linear approach significantly improves upon the existing revised Framingham stroke risk calculator in the c-statistic (training 87.43% (CI 0.85-0.90) vs. 73.74% (CI 0.70-0.76); validation 75.29% (CI 0.74-0.76) vs 65.93% (CI 0.64-0.67), even in multi-ethnicity populations. Conclusions: We constructed a highly predictive, interpretable and user-friendly stroke risk calculator using novel machine-learning uncovering new risk factors, interactions and unique profiles. The clinical implications include prioritization of risk factor modification and personalized care improving targeted intervention for stroke prevention.

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Improving stroke risk prediction in the general population: Common clinical rules, a new multimorbid index and machine learning based algorithms

Thrombosis and Haemostasis ◽

10.1055/a-1467-2993 ◽

2021 ◽

Author(s):

Gregory YH Lip ◽

Ash Genaidy ◽

George Tran ◽

Patricia Marroquin ◽

Cara Estes ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Discriminant Validity ◽

Stroke Risk ◽

External Validation ◽

Risk Scores ◽

Dynamic Nature ◽

Stroke Outcomes ◽

Complex Relationships ◽

Clinical Rules

We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach accounting for the complex relationships among variables, including the dynamic nature of changing risk factors. Methods We studied a prospective US cohort of 3435224 patients from medical databases in a 2-year investigation. Stroke outcomes were examined in relationship to diverse multi-morbid conditions, demographic variables and other inputs, with ML accounting for the dynamic nature of changing multimorbidity risk factors, 2 clinical risk scores and a clinical multimorbid index. Results Common clinical risk scores had moderate and comparable c indices with stroke outcomes in the training and external validation samples (validation – CHADS2: c index 0.812; CHA2DS2-VASc: c index 0.809). A clinical multimorbid index had higher discriminant validity values for both the training/external validation samples (validation: c-index 0.850). The machine learning (ML) based algorithms yielded the highest discriminant validity values for the gradient boosting/neural network logistic regression formulations with no significant differences among the ML approaches (validation for logistic regression: c index 0.866). Calibration of the ML based formulation was satisfactory across a wide range of predicted probabilities. Decision curve analysis demonstrated that clinical utility for the ML based formulation was best. Also, ML models and clinical stroke risk scores were more clinically useful than the ‘treat all’ strategy. Conclusion Complex relationships of various comorbidities uncovered using a ML approach for diverse(and dynamic) multimorbidity changes have major consequences for stroke risk prediction.

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0832 Evaluating Supervised Machine Learning Models for Cardiovascular Disease Prediction Using Conventional Risk Factors, Apnea-Hypopnea Index and Epworth Sleepiness Scale

SLEEP ◽

10.1093/sleep/zsz067.830 ◽

2019 ◽

Vol 42 (Supplement_1) ◽

pp. A334-A334 ◽

Cited By ~ 1

Author(s):

Diego R Mazzotti ◽

Brendan T Keenan ◽

Ryan Urbanowicz ◽

Allan I Pack

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Cardiovascular Disease ◽

Epworth Sleepiness Scale ◽

Supervised Machine Learning ◽

Apnea Hypopnea Index ◽

Disease Prediction ◽

Learning Models ◽

Conventional Risk Factors ◽

Machine Learning Models

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Using machine learning models to improve stroke risk level classification methods of China national stroke screening

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0998-2 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Xuemeng Li ◽

Di Bian ◽

Jinghui Yu ◽

Mei Li ◽

Dongsheng Zhao

Keyword(s):

Machine Learning ◽

Risk Factors ◽

High Risk ◽

Random Forest ◽

Stroke Risk ◽

Classification Models ◽

Training Set ◽

Tree Model ◽

Risk Levels ◽

History Of

Abstract Background With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency. Method Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels. Result The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year. Conclusion Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

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