scholarly journals Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults

2021 ◽  
Vol 10 (21) ◽  
pp. 5184
Author(s):  
Keitaro Makino ◽  
Sangyoon Lee ◽  
Seongryu Bae ◽  
Ippei Chiba ◽  
Kenji Harada ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Tree ◽  
Predictive Value ◽  
Community Dwelling ◽  
Decision Tree Model ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Tree Algorithm ◽  
Fall Prediction ◽  
Community Dwelling Older Adults

The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.

Sea-Battlefield Situation Assessment Based on Improved Decision Tree

2014 ◽  
Vol 574 ◽  
pp. 639-645
Author(s):  
Yong Tao Yu ◽  
Ying Ding
Keyword(s):  
Decision Tree ◽  
Decision Tree Model ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Situation Assessment ◽  
Tree Algorithm ◽  
Initial Decision

How to efficiently evaluate the dynamic changing sea-battlefield is the key of command decision. According to research sea-battlefield situation assessment based on improved decision tree algorithm based on derived attributes. First is based on the decision tree algorithm to establish the sea-battlefield situation assessment initial decision tree. Second are dynamically generated derivative branches set based on derived attributes. Once again, it can be grafting and pruning derivative branches to form the sea-battlefield situation assessment derived decision tree model. Finally, it may use a derived dynamically decision tree model assess sea-battlefield in different time slices.

scholarly journals Polish Translation and Validation of the Mini Sarcopenia Risk Assessment (MSRA) Questionnaire to Assess Nutritional and Non-Nutritional Risk Factors of Sarcopenia in Older Adults

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1061
Author(s):  
Roma Krzymińska-Siemaszko ◽  
Ewa Deskur-Śmielecka ◽  
Arkadiusz Styszyński ◽  
Katarzyna Wieczorowska-Tobis
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Risk Assessment ◽  
Predictive Value ◽  
High Sensitivity ◽  
Community Dwelling ◽  
Nutritional Risk ◽  
Polish Language ◽  
Community Dwelling Older Adults ◽  
Translation And Validation

A simple, short, cheap, and reasonably sensitive and specific screening tool assessing both nutritional and non-nutritional risk factors for sarcopenia is needed. Potentially, such a tool may be the Mini Sarcopenia Risk Assessment (MSRA) Questionnaire, which is available in a seven-item (MSRA-7) and five-item (MSRA-5) version. The study’s aim was Polish translation and validation of both MSRA versions in 160 volunteers aged ≥60 years. MSRA was validated against the six sets of international diagnostic criteria for sarcopenia used as the reference standards. PL-MSRA-7 and PL-MSRA-5 both had high sensitivity (≥84.9%), regardless of the reference standard. The PL-MSRA-5 had better specificity (44.7–47.2%) than the PL-MSRA-7 (33.1–34.7%). Both questionnaires had similarly low positive predictive value (PL-MSRA-5: 17.9–29.5%; PL-MSRA-7: 14.4–25.2%). The negative predictive value was generally high for both questionnaires (PL-MSRA-7: 89.8–95.9%; PL-MSRA-5: 92.3–98.5%). PL-MSRA-5 had higher accuracy than the PL-MSRA-7 (50.0–55% vs. 39.4–45%, respectively). Based on the results, the Mini Sarcopenia Risk Assessment questionnaire was successfully adopted to the Polish language and validated in community-dwelling older adults from Poland. When compared with PL-MSRA-7, PL-MSRA-5 is a better tool for sarcopenia risk assessment.

scholarly journals Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Early Stage ◽  
Current Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Decision Tree Model ◽  
Tree Model ◽  
Chinese Adult ◽  
Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

scholarly journals Development and validation of new screening tool for predicting dementia risk in community-dwelling older Japanese adults

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Keitaro Makino ◽  
Sangyoon Lee ◽  
Seongryu Bae ◽  
Ippei Chiba ◽  
Kenji Harada ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression Model ◽  
Screening Tool ◽  
Community Dwelling ◽  
Tree Model ◽  
Face To Face ◽  
Dementia Risk ◽  
Dementia Incidence ◽  
Community Dwelling Older Adults

Abstract Background Established clinical assessments for detecting dementia risk often require time, cost, and face-to-face meetings. We aimed to develop a Simplified Telephone Assessment for Dementia risk (STAD) (a new screening tool utilizing telephonic interviews to predict dementia risk) and examine the predictive validity of the STAD for the incidence of dementia. Methods We developed STAD based on a combination of literature review, statistical analysis, and expert opinion. We selected 12 binary questions on subjective cognitive complaints, depressive symptoms, and lifestyle activities. In the validation study, we used STAD for 4298 community-dwelling older adults and observed the incidence of dementia during the 24-month follow-up period. The total score of STAD ranging from 0 to 12 was calculated, and the cut-off point for dementia incidence was determined using the Youden index. The survival rate of dementia incidence according to the cut-off points was determined. Furthermore, we used a decision-tree model (classification and regression tree, CART) to enhance the predictive ability of STAD for dementia risk screening. Results The cut-off point of STAD was set at 4/5. Participants scoring ≥ 5 points showed a significantly higher risk of dementia than those scoring ≤ 4 points, even after adjusting for covariates (hazard ratio [95% confidence interval], 2.67 [1.40–5.08]). A decision tree model using the CART algorithm was constructed using 12 nodes with three STAD items. It showed better performance for dementia prediction in terms of accuracy and specificity as compared to the logistic regression model, although its sensitivity was worse than the logistic regression model. Conclusions We developed a 12-item questionnaire, STAD, as a screening tool to predict dementia risk utilizing telephonic interviews and confirmed its predictive validity. Our findings might provide useful information for early screening of dementia risk and enable bridging between community and clinical settings. Additionally, STAD could be employed without face-to-face meetings in a short time; therefore, it may be a suitable screening tool for community-dwelling older adults who have negative attitudes toward clinical examination or are non-adherent to follow-up assessments in clinical trials.

scholarly journals Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm

2018 ◽  
Vol 13 (10) ◽  
pp. 146 ◽  
Author(s):  
Sujuan Jia ◽  
Yajing Pang
Keyword(s):  
Decision Tree ◽  
Quality Evaluation ◽  
Computation Time ◽  
Teaching Quality ◽  
Quality Data ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Tree Algorithm ◽  
Higher Education System ◽  
C4.5 Decision Tree

Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner’s personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learn-er’s personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner’s personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statisti-cal data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed.

Enhancement of the Chinese Movement Specific Reinvestment Scale in Older Adults

GeroPsych ◽  
2015 ◽  
Vol 28 (3) ◽  
pp. 137-141 ◽  
Author(s):  
Thomson W. L. Wong ◽  
Bruce Abernethy ◽  
Rich S. W. Masters
Keyword(s):  
Older Adults ◽  
Focus Group ◽  
Qualitative Study ◽  
Community Dwelling ◽  
Prediction Tool ◽  
Response Format ◽  
Discriminative Power ◽  
Motor Processing ◽  
Fall Prediction ◽  
Community Dwelling Older Adults

Abstract. The Chinese version of the Movement Specific Reinvestment Scale (MSRS-C) was discovered to have good discriminative power between older fallers from nonfallers, and it shows potential as a novel fall prediction tool by assessing conscious motor processing propensity of the older adults. This qualitative study (focus group) investigated potential weaknesses during the application of the MSRS-C in community-dwelling older adults. The results confirmed two major potential weaknesses of the MSRS-C: older adults may respond differently when asked to complete the MSRS-C in the context of movements related or unrelated to balance or locomotion; older adults may be better able to differentiate a 4-point Likert response format than the original 6-point format MSRS-C. Further study was developed to examine the identified potential weaknesses.

scholarly journals Decision Tree Model for Rockburst Prediction Based on Microseismic Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hongbo Zhao ◽  
Bingrui Chen ◽  
Changxing Zhu
Keyword(s):  
Decision Tree ◽  
Dynamic Instability ◽  
Microseismic Monitoring ◽  
Decision Tree Model ◽  
Monitoring Data ◽  
Underground Excavation ◽  
Decision Tree Algorithm ◽  
Complex Dynamic ◽  
Tree Model ◽  
The Relationship

Rockburst is an extremely complex dynamic instability phenomenon for rock underground excavation. It is difficult to predict and evaluate the rank level of rockburst in practice. Microseismic monitoring technology has been adopted to obtain microseismic events of microcrack in rock mass for rockburst. The possibility of rockburst can be reflected by microseismic monitoring data. In this study, a decision tree was used to extract the knowledge of rockburst from microseismic monitoring data. The predictive model of rockburst was built based on microseismic monitoring data using a decision tree algorithm. The predictive results were compared with the real rank of rockburst. The relationship between rockburst and microseismic feature data was investigated using the developed decision tree model. The results show that the decision tree can extract the rockburst feature from the microseismic monitoring data. The rockburst is predictable based on microseismic monitoring data. The decision tree provides a feasible and promising approach to predict and evaluate rockburst.

Predictive value of intrinsic capacity on adverse outcomes among community-dwelling older adults

2021 ◽  
Vol 42 (6) ◽  
pp. 1257-1263
Author(s):  
Jiaqi Yu ◽  
Huaxin Si ◽  
Xiaoxia Qiao ◽  
Yaru Jin ◽  
Lili Ji ◽  
...  
Keyword(s):  
Older Adults ◽  
Predictive Value ◽  
Adverse Outcomes ◽  
Community Dwelling ◽  
Intrinsic Capacity ◽  
Community Dwelling Older Adults

scholarly journals Assessment of Neck Grasp as a Screening Tool for Identifying Obstructive Sleep Apnea in Community-Dwelling Older Adults

2020 ◽  
Vol 11 ◽  
pp. 215013272098442
Author(s):  
Oscar H. Del Brutto ◽  
Robertino M. Mera ◽  
Bettsy Y. Recalde ◽  
Pablo R. Castillo
Keyword(s):  
Older Adults ◽  
Obstructive Sleep Apnea ◽  
Logistic Regression ◽  
Sleep Apnea ◽  
Predictive Value ◽  
Regression Models ◽  
Community Dwelling ◽  
Logistic Regression Models ◽  
Obstructive Sleep ◽  
Community Dwelling Older Adults

Background Inability to encircle the neck by hands (neck grasp) has been proposed as an indicator of obstructive sleep apnea (OSA) that would be useful for recognition of candidates for polysomnography (PSG). We assessed the value of neck grasp for predicting OSA in community-dwelling older adults of Amerindian ancestry. Methods Neck grasp was evaluated in individuals aged ≥60 years undergoing PSG. The association between neck grasp and OSA was assessed by logistic regression models adjusted for relevant covariates. Mediation analysis was used to establish the proportion of the effect of the association between neck grasp and OSA, which is mediated by the neck circumference (a well-known OSA biomarker). Receiver operator characteristics curve analysis was used to estimate diagnostic accuracy of neck grasp for predicting OSA. Results Of 201 individuals undergoing PSG, 167 (83%) had the neck grasp test. The remaining 34 could not perform the test because of different factors. Neck grasp was positive in 127 (76%) cases, and 114 (68%) individuals had OSA (apnea-hypopnea index ≥5). Multivariate logistic regression models disclosed a significant association between neck grasp and OSA. The neck circumference was the single covariate remaining independently significant in these models. Neck grasp was not efficient at predicting OSA (sensitivity: 83.3%, specificity: 39.6%, positive predictive value: 0.75 and negative predictive value: 0.53). The area under the curve disclosed only a moderate predictive capability (61.5%) of neck grasp. Conclusion Results do not support the use of neck grasp as an independent predictor of OSA in the study population.

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