Construction and Evaluation of Bayesian Networks Related to the Specific Health Checkup and Guidance on Metabolic Syndrome

2015 ◽  
pp. 183-193 ◽  
Author(s):  
Yoshiaki Miyauchi ◽  
Haruhiko Nishimura
Keyword(s):  
Metabolic Syndrome ◽  
Bayesian Networks ◽  
Health Checkup ◽  
Specific Health

scholarly journals Association between the type of physical activity and metabolic syndrome in middle-aged and older adult residents of a semi-mountainous area in Japan

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Noriko Kudo ◽  
Ritsuko Nishide ◽  
Mayumi Mizutani ◽  
Shota Ogawa ◽  
Susumu Tanimura
Keyword(s):  
Physical Activity ◽  
Metabolic Syndrome ◽  
Older Women ◽  
Secondary Data ◽  
Daily Physical Activity ◽  
Mountainous Area ◽  
Mountainous Areas ◽  
Middle Aged ◽  
Type Activity ◽  
Specific Health

Abstract Background Physical activity is reported to prevent metabolic syndrome. However, it is unclear whether exercise or daily physical activity is more beneficial for residents of semi-mountainous areas. This study aimed to identify whether daily physical activity is more beneficial than exercise for the prevention of metabolic syndrome among middle-aged and older residents in semi-mountainous areas. Methods We analyzed secondary data of 636 people who underwent a specific health checkup in a semi-mountainous area of Japan. Physical activity was classified into four types: inactivity (I-type; without exercise and without daily physical activity), only exercise (E-type; with exercise and without daily physical activity), only daily physical activity (D-type; without exercise and with daily physical activity), and full physical activity type (F-type; with exercise and with daily physical activity). We compared the means of risk factors for metabolic syndrome by these four types, followed by logistic regression analysis, to identify whether and to what extent the D-type was less likely to have metabolic syndrome than the E-type. Results The prevalence of metabolic syndrome was 28.5% (men 45.7%, women 15.8%). The proportions of men with exercise and daily physical activity were 38.7% and 52.8%, respectively. For women, the proportions were 33.0% and 47.1%, respectively. In women, the D-type had the significantly lowest BMI, smallest waist circumference, highest HDL-C, and lowest prevalence of metabolic syndrome of the four types; the same was not observed in men. Additionally, D-type activity was more strongly associated with a reduced risk of metabolic syndrome than E-type activity in women (adjusted odds ratio 0.24; 95% confidence interval 0.06–0.85, P = 0.028). Conclusions Compared to middle-aged and older women residents with exercise in a semi-mountainous area of Japan, those with daily physical activity may effectively prevent metabolic syndrome.

scholarly journals SAT-LB121 Development of a Machine-Learning Method for Predicting New Onset of Diabetes Mellitus: A Retrospective Analysis of 509,153 Annual Specific Health Checkup Records

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Akihiro Nomura ◽  
Sho Yamamoto ◽  
Yuta Hayakawa ◽  
Kouki Taniguchi ◽  
Takuya Higashitani ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Prediction Model ◽  
Performance Test ◽  
Bootstrap Method ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Health Checkup ◽  
Specific Health

Abstract Diabetes mellitus (DM) is a chronic disorder, characterized by impaired glucose metabolism. It is linked to increased risks of several diseases such as atrial fibrillation, cancer, and cardiovascular diseases. Therefore, DM prevention is essential. However, the traditional regression-based DM-onset prediction methods are incapable of investigating future DM for generally healthy individuals without DM. Employing gradient-boosting decision trees, we developed a machine learning-based prediction model to identify the DM signatures, prior to the onset of DM. We employed the nationwide annual specific health checkup records, collected during the years 2008 to 2018, from Kanazawa city, Ishikawa, Japan. The data included the physical examinations, blood and urine tests, and participant questionnaires. Individuals without DM (at baseline), who underwent more than two annual health checkups during the said period, were included. The new cases of DM onset were recorded when the participants were diagnosed with DM in the annual check-ups. The dataset was divided into three subsets in a 6:2:2 ratio to constitute the training, tuning (internal validation), and testing datasets. Employing the testing dataset, the ability of our trained prediction model to calculate the area under the curve (AUC), precision, recall, F1 score, and overall accuracy was evaluated. Using a 1,000-iteration bootstrap method, every performance test resulted in a two-sided 95% confidence interval (CI). We included 509,153 annual health checkup records of 139,225 participants. Among them, 65,505 participants without DM were included, which constituted36,303 participants in the training dataset and 13,101 participants in each of the tuning and testing datasets. We identified a total of 4,696 new DM-onset patients (7.2%) in the study period. Our trained model predicted the future incidence of DM with the AUC, precision, recall, F1 score, and overall accuracy of 0.71 (0.69-0.72 with 95% CI), 75.3% (71.6-78.8), 42.2% (39.3-45.2), 54.1% (51.2-56.7), and 94.9% (94.5-95.2), respectively. In conclusion, the machine learning-based prediction model satisfactorily identified the DM onset prior to the actual incidence.

scholarly journals Relationship between Subjective Masticatory Ability and Metabolic Syndrome in Specific Health Check-Up Participants

2010 ◽  
Vol 25 (4) ◽  
pp. 533-537
Author(s):  
Daisuke URITANI ◽  
Daisuke MATSUMOTO ◽  
Yasuyo ASANO
Keyword(s):  
Metabolic Syndrome ◽  
Health Check ◽  
Masticatory Ability ◽  
Specific Health

scholarly journals The Effect of the Treatment on the Risk Factors of Arteriosclerotic Diseases in the Elderly: Investigating the Specific Health Checkup

2015 ◽  
Vol 6 (1) ◽  
pp. 41-45
Author(s):  
Keito Torikai ◽  
Nobuyoshi Narita ◽  
Yuko Tohyo ◽  
Masatoshi Hara ◽  
Takahide Matsuda
Keyword(s):  
Risk Factors ◽  
The Elderly ◽  
Health Checkup ◽  
Specific Health
Sign in / Sign up

Export Citation Format

Share Document