scholarly journals A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

10.2196/20298 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e20298
Author(s):  
Mingyue Hu ◽  
Xinhui Shu ◽  
Gang Yu ◽  
Xinyin Wu ◽  
Maritta Välimäki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cognitive Impairment ◽  
Older People ◽  
Prediction Model ◽  
Elderly People ◽  
Community Dwelling ◽  
Chinese Community ◽  
Model Based ◽  
Normal Cognition

Background Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging. Objective We aimed to build a prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition. Methods A prospective cohort of 6718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) register, followed between 2008 and 2011, was used to develop and validate the prediction model. Participants were included if they were aged 60 years or above, were community-dwelling elderly people, and had a cognitive Mini-Mental State Examination (MMSE) score ≥18. They were excluded if they were diagnosed with a severe disease (eg, cancer and dementia) or were living in institutions. Cognitive impairment was identified using the Chinese version of the MMSE. Several machine learning algorithms (random forest, XGBoost, naïve Bayes, and logistic regression) were used to assess the 3-year risk of developing cognitive impairment. Optimal cutoffs and adjusted parameters were explored in validation data, and the model was further evaluated in test data. A nomogram was established to vividly present the prediction model. Results The mean age of the participants was 80.4 years (SD 10.3 years), and 50.85% (3416/6718) were female. During a 3-year follow-up, 991 (14.8%) participants were identified with cognitive impairment. Among 45 features, the following four features were finally selected to develop the model: age, instrumental activities of daily living, marital status, and baseline cognitive function. The concordance index of the model constructed by logistic regression was 0.814 (95% CI 0.781-0.846). Older people with normal cognitive functioning having a nomogram score of less than 170 were considered to have a low 3-year risk of cognitive impairment, and those with a score of 170 or greater were considered to have a high 3-year risk of cognitive impairment. Conclusions This simple and feasible cognitive impairment prediction model could identify community-dwelling elderly people at the greatest 3-year risk for cognitive impairment, which could help community nurses in the early identification of dementia.

scholarly journals Development and validation of risk prediction model for cognitive impairment in Chinese community dwellers with normal cognition: using machine learning approach (Preprint)

2020 ◽  
Author(s):  
Mingyue Hu ◽  
Xinyin Wu ◽  
Xinhui Shu ◽  
Yinan Zhao ◽  
Hui Feng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cognitive Impairment ◽  
Cognitive Function ◽  
Older People ◽  
Prediction Model ◽  
Develop Model ◽  
Chinese Elderly ◽  
Normal Cognition ◽  
Community Dwellers

BACKGROUND Dementia causes huge pressure on families and goverments worldwide. Early detection of individuals at risk of cognitive impairment is critical to reduce the mortality rate. OBJECTIVE We aimed to build a prediction model based on machine learning for cognitive impairment (CI) in Chinese elderly community dwellers with normal cognition. METHODS A prospective cohort of 6,718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), followed from 2008-2011, was used to develop and validate the prediction model. CI was identified using Chinese version of the Mini-Mental State Examination (CMMSE). Several machine learning algorithms (Random Forest, XGBoost, Naïve Bayes and logistic regression) were used to model 3-year risk of CI. We explored optimal cutoffs and adjusted parameters in validation data and evaluated the model in test data. Nomogram was established to vividly present the prediction model. RESULTS Mean age was 80.4 ± 10.3 years and 50.8% were female. During 3-year follow-up, 991 (14.8%) participants were identified as CI. Four features were finally selected to develop model, including age, IADL, marital status and baseline cognitive function. The Concordance index of the model constructed by logistic regression were 0.814 (95%CI 0.781 - 0.846). Older people with normal cognitive function who had a nomogram score of less than 170 or 170 or greater were considered to have low or high 3-year risks of CI, respectively. CONCLUSIONS The simple and feasible CI prediction model could identify Chinese elderly community dwellers at greatest risk for CI. This practical model presented by nomogram could be used to screen Chinese elderly community dwellers for CI and to target intervention strategies.

scholarly journals Prediction of 11-year incidence of psychophysically dependent status or death among community-dwelling younger elderlies: from an age-specified community-based cohort study (the NISSIN project)

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Satoe Okabayashi ◽  
Takashi Kawamura ◽  
Hisashi Noma ◽  
Kenji Wakai ◽  
Masahiko Ando ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Cohort Study ◽  
Prediction Model ◽  
Nursing Care ◽  
Family Members ◽  
Limited Resource ◽  
Community Dwelling ◽  
Older Individuals ◽  
Regression Analyses

Abstract Background Predicting adverse health events and implementing preventative measures are a necessary challenge. It is important for healthcare planners and policymakers to allocate the limited resource to high-risk persons. Prediction is also important for older individuals, their family members, and clinicians to prepare mentally and financially. The aim of this study is to develop a prediction model for within 11-year dependent status requiring long-term nursing care or death in older adults for each sex. Methods We carried out age-specified cohort study of community dwellers in Nisshin City, Japan. The older adults aged 64 years who underwent medical check-up between 1996 and 2005 were included in the study. The primary outcome was the incidence of the psychophysically dependent status or death or by the end of the year of age 75 years. Univariable logistic regression analyses were performed to assess the associations between candidate predictors and the outcome. Using the variables with p-values less than 0.1, multivariable logistic regression analyses were then performed with backward stepwise elimination to determine the final predictors for the model. Results Of the 1525 female participants at baseline, 105 had an incidence of the study outcome. The final prediction model consisted of 15 variables, and the c-statistics for predicting the outcome was 0.763 (95% confidence interval [CI] 0.714–0.813). Of the 1548 male participants at baseline, 211 had incidence of the study outcome. The final prediction model consisted of 16 variables, and the c-statistics for predicting the outcome was 0.735 (95% CI 0.699–0.771). Conclusions We developed a prediction model for older adults to forecast 11-year incidence of dependent status requiring nursing care or death in each sex. The predictability was fair, but we could not evaluate the external validity of this model. It could be of some help for healthcare planners, policy makers, clinicians, older individuals, and their family members to weigh the priority of support.

Abstract MP003: Development and Validation of a Prediction Model of Weight Loss Maintenance or Regain at 4 Years in the Look AHEAD Trial

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Samantha E Berger ◽  
Gordon S Huggins ◽  
Jeanne M McCaffery ◽  
Alice H Lichtenstein
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Weight Loss ◽  
Logistic Regression ◽  
Prediction Model ◽  
Weight Regain ◽  
Initial Weight ◽  
Initial Weight Loss ◽  
Look Ahead

Introduction: The development of type 2 diabetes is strongly associated with excess weight gain and can often be partially ameliorated or reversed by weight loss. While many lifestyle interventions have resulted in successful weight loss, strategies to maintain the weight loss have been considerably less successful. Prior studies have identified multiple predictors of weight regain, but none have synthesized them into one analytic stream. Methods: We developed a prediction model of 4-year weight regain after a one-year lifestyle-induced weight loss intervention followed by a 3 year maintenance intervention in 1791 overweight or obese adults with type 2 diabetes from the Action for Health in Diabetes (Look AHEAD) trial who lost ≥3% of initial weight by the end of year 1. Weight regain was defined as regaining <50% of the weight lost during the intervention by year 4. Using machine learning we integrated factors from several domains, including demographics, psychosocial metrics, health status and behaviors (e.g. physical activity, self-monitoring, medication use and intervention adherence). We used classification trees and stochastic gradient boosting with 10-fold cross validation to develop and internally validate the prediction model. Results: At the end of four years, 928 individuals maintained ≥50% of their initial weight lost (maintainers), whereas 863 did not met that criterion (regainers). We identified an interaction between age and several variables in the model, as well as percent initial weight loss. Several factors were significant predictors of weight regain based on variable importance plots, regardless of age or initial weight loss, such as insurance status, physical function score, baseline BMI, meal replacement use and minutes of exercise recorded during year 1. We also identified several factors that were significant predictors depending on age group (45-55y/ 56-65y/66-76y) and initial weight loss (lost 3-9% vs. ≥10% of initial weight). When the variables identified from machine learning were added to a logistic regression model stratified by age and initial weight loss groups, the models showed good prediction (3-9% initial weight loss, ages 45-55y (n=293): ROC AUC=0.78; ≥10% initial weight loss, ages 45-55y (n=242): ROC AUC=0.78; (3-9% initial weight loss, ages 56-65y (n=484): ROC AUC=0.70; ≥10% initial weight loss, ages 56-65y (n=455): ROC AUC = 0.74; 3-9% initial weight loss, ages 66-76y (n=150): ROC AUC=0.84; ≥10% initial weight loss, ages 66-76y (n=167): ROC AUC=0.86). Conclusion: The combination of machine learning methodology and logistic regression generates a prediction model that can consider numerous factors simultaneously, can be used to predict weight regain in other populations and can assist in the development of better strategies to prevent post-loss regain.

Visual Attention Prediction Model Based on Prominence Maps, Machine Learning and Biometric Data

2021 ◽  
Author(s):  
Helver Novoa Mendoza ◽  
William Joseph Giraldo ◽  
Emilio Granell ◽  
Faber Danilo Giraldo
Keyword(s):  
Machine Learning ◽  
Visual Attention ◽  
Prediction Model ◽  
Biometric Data ◽  
Model Based

scholarly journals Supporting community‐dwelling older people with cognitive impairment to stay at home: A modelled cost analysis

2020 ◽  
Vol 39 (4) ◽  
Author(s):  
Suzanne M. Dyer ◽  
Lachlan B. Standfield ◽  
Nicola Fairhall ◽  
Ian D. Cameron ◽  
Meredith Gresham ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cost Analysis ◽  
Older People ◽  
Community Dwelling ◽  
Community Dwelling Older People ◽  
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