Logistic regression was as good as machine learning for predicting major chronic diseases.
By S. Nusinovici, Y. Tham, Marco Yu Chak Yan, Daniel Shu Wei Ting, Jialiang Li, C. Sabanayagam, Tien Yin Wong, Ching-Yu Cheng. Journal of Clinical Epidemiology, 2020
457 citations recorded by the source corpus.
OBJECTIVE To evaluate the performance of machine learning (ML) algorithms and to compare them to logistic regression for the prediction of risk of cardiovascular diseases (CVD), chronic kidney disease (CKD), diabetes (DM), and hypertension (HTN) and in a prospective cohort study using simple clinical predictors. STUDY DESIGN AND SETTING We conducted analyses in a population-based cohort study in Asian adults (n=6,762). Five different ML models were considered: single-hidden-layer neural network, support vector machine, random forest, gradient boosting machine and k-nearest neighbour, and were compared to standard logistic regression. RESULTS The incidences at 6-year of CVD, CKD, DM, and HTN cases were 4.0%, 7.0%, 9.2%, and 34.6%, respectively. Logistic regression reached the highest AUC for CKD (0.905 [0.88, 0.93]) and DM (0.768 [0.73, 0.81]) predictions. For CVD and HTN, the best models were neural network (0.753 [0.70, 0.81]) and support vector machine (0.780 [0.747, 0.812]), respectively. However, the differences with logistic regression were small (less than 1%) and non-significant. Logistic regression, gradient boosting machine and neural network were systematically ranked among the best models. CONCLUSION Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors.
Subfields: Artificial Intelligence, Health Informatics, Health Information Management
View Paper: https://www.semanticscholar.org/paper/b38e5ef006e4f5be37a078c1900c8cbc920b624d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
Citations in the corpus
- The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “Several studies found no benefit of machine learning over tra ditional logistic regression, for example in predicting treatment resistance in major depression 11 , brain injury outcomes 12 , or ma jor chronic diseases 13 .”