Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study | Scientific Reports – Nature.com

Participants

This study was conducted as part of the ongoing Study on the Design of a Comprehensive Medical System for Chronic Kidney Disease (CKD) Based on Individual Risk Assessment by Specific Health Examination (J-SHC Study). A specific health checkup is conducted annually for all residents aged 4074years, covered by the National Health Insurance in Japan. In this study, a baseline survey was conducted in 685,889 people (42.7% males, age 4074years) who participated in specific health checkups from 2008 to 2014 in eight regions (Yamagata, Fukushima, Niigata, Ibaraki, Toyonaka, Fukuoka, Miyazaki, and Okinawa prefectures). The details of this study have been described elsewhere11. Of the 685,889 baseline participants, 169,910 were excluded from the study because baseline data on lifestyle information or blood tests were not available. In addition, 399,230 participants with a survival follow-up of fewer than 5years from the baseline survey were excluded. Therefore, 116,749 patients (42.4% men) with a known 5-year survival or mortality status were included in this study.

This study was conducted in accordance with the Declaration of Helsinki guidelines. This study was approved by the Ethics Committee of Yamagata University (Approval No. 2008103). All data were anonymized before analysis; therefore, the ethics committee of Yamagata University waived the need for informed consent from study participants.

For the validation of a predictive model, the most desirable way is a prospective study on unknown data. In this study, the data on health checkup dates were available. Therefore, we divided the total data into training and test datasets to build and test predictive models based on health checkup dates. The training dataset consisted of 85,361 participants who participated in the study in 2008. The test dataset consisted of 31,388 participants who participated in this study from 2009 to 2014. These datasets were temporally separated, and there were no overlapping participants. This method would evaluate the model in a manner similar to a prospective study and has an advantage that can demonstrate temporal generalizability. Clipping was performed for 0.01% outliers for preprocessing, and normalization was performed.

Information on 38 variables was obtained during the baseline survey of the health checkups. When there were highly correlated variables (correlation coefficient greater than 0.75), only one of these variables was included in the analysis. High correlations were found between body weight, abdominal circumference, body mass index, hemoglobin A1c (HbA1c), fasting blood sugar, and AST and alanine aminotransferase (ALT) levels. We then used body weight, HbA1c level, and AST level as explanatory variables. Finally, we used the following 34 variables to build the prediction models: age, sex, height, weight, systolic blood pressure, diastolic blood pressure, urine glucose, urine protein, urine occult blood, uric acid, triglycerides, high-density lipoprotein cholesterol (HDL-C), LDL-C, AST, -glutamyl transpeptidase (GTP), estimated glomerular filtration rate (eGFR), HbA1c, smoking, alcohol consumption, medication (for hypertension, diabetes, and dyslipidemia), history of stroke, heart disease, and renal failure, weight gain (more than 10kg since age 20), exercise (more than 30min per session, more than 2days per week), walking (more than 1h per day), walking speed, eating speed, supper 2h before bedtime, skipping breakfast, late-night snacks, and sleep status.

The values of each item in the training data set for the alive/dead groups were compared using the chi-square test, Student t-test, and MannWhitney U test, and significant differences (P<0.05) were marked with an asterisk (*) (Supplementary Tables S1 and S2).

We used two machine learning-based methods (gradient boosting decision tree [XGBoost], neural network) and one conventional method (logistic regression) to build the prediction models. All the models were built using Python 3.7. We used the XGBoost library for GBDT, TensorFlow for neural network, and Scikit-learn for logistic regression.

The data obtained in this study contained missing values. XGBoost can be trained to predict even with missing values because of its nature; however, neural network and logistic regression cannot be trained to predict with missing values. Therefore, we complemented the missing values using the k-nearest neighbor method (k=5), and the test data were complemented using an imputer trained using only the training data.

The parameters required for each model were determined for the training data using the RandomizedSearchCV class of the Scikit-learn library and repeating fivefold cross-validation 5000 times.

The performance of each prediction model was evaluated by predicting the test dataset, drawing a ROC curve, and using the AUC. In addition, the accuracy, precision, recall, F1 scores (the harmonic mean of precision and recall), and confusion matrix were calculated for each model. To assess the importance of explanatory variables for the predictive models, we used SHAP and obtained SHAP values that express the influence of each explanatory variable on the output of the model4,12. The workflow diagram of this study is shown in Fig.5.

Workflow diagram of development and performance evaluation of predictive models.

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Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study | Scientific Reports - Nature.com