A data-driven approach to predicting diabetes and cardiovascular disease with machine learning

Autor: Stacey Miertschin, Somya D. Mohanty, Amber Young, An Dinh
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Male
Health analytics
Support Vector Machine
National Health and Nutrition Examination Survey
Health Informatics
02 engineering and technology
lcsh:Computer applications to medicine. Medical informatics
Machine learning
computer.software_genre
Logistic regression
Machine Learning
03 medical and health sciences
0302 clinical medicine
Predictive Value of Tests
Ensemble learning
0202 electrical engineering
electronic engineering
information engineering
Diabetes Mellitus
Medicine
Electronic Health Records
Humans
030212 general & internal medicine
Receiver operating characteristic
business.industry
Health Policy
Feature learning
Nutrition Surveys
3. Good health
Computer Science Applications
Random forest
Support vector machine
Logistic Models
ROC Curve
Cardiovascular Diseases
lcsh:R858-859.7
020201 artificial intelligence & image processing
Female
Gradient boosting
Artificial intelligence
business
computer
Research Article
Zdroj: BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-15 (2019)
ISSN: 1472-6947
Popis: Background Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients. Methods Our research explores data-driven approaches which utilize supervised machine learning models to identify patients with such diseases. Using the National Health and Nutrition Examination Survey (NHANES) dataset, we conduct an exhaustive search of all available feature variables within the data to develop models for cardiovascular, prediabetes, and diabetes detection. Using different time-frames and feature sets for the data (based on laboratory data), multiple machine learning models (logistic regression, support vector machines, random forest, and gradient boosting) were evaluated on their classification performance. The models were then combined to develop a weighted ensemble model, capable of leveraging the performance of the disparate models to improve detection accuracy. Information gain of tree-based models was used to identify the key variables within the patient data that contributed to the detection of at-risk patients in each of the diseases classes by the data-learned models. Results The developed ensemble model for cardiovascular disease (based on 131 variables) achieved an Area Under - Receiver Operating Characteristics (AU-ROC) score of 83.1% using no laboratory results, and 83.9% accuracy with laboratory results. In diabetes classification (based on 123 variables), eXtreme Gradient Boost (XGBoost) model achieved an AU-ROC score of 86.2% (without laboratory data) and 95.7% (with laboratory data). For pre-diabetic patients, the ensemble model had the top AU-ROC score of 73.7% (without laboratory data), and for laboratory based data XGBoost performed the best at 84.4%. Top five predictors in diabetes patients were 1) waist size, 2) age, 3) self-reported weight, 4) leg length, and 5) sodium intake. For cardiovascular diseases the models identified 1) age, 2) systolic blood pressure, 3) self-reported weight, 4) occurrence of chest pain, and 5) diastolic blood pressure as key contributors. Conclusion We conclude machine learned models based on survey questionnaire can provide an automated identification mechanism for patients at risk of diabetes and cardiovascular diseases. We also identify key contributors to the prediction, which can be further explored for their implications on electronic health records.
Databáze: OpenAIRE
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