Methods: The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.
Results: The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R2) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO2, SO2, PM10, rainfall, temperature, and atmospheric pressure, with the highest adjusted R2 value of 0.47, errors below 6, and accuracies above 96%.
Conclusions: It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.
METHODS: We used the 11-item Duke Social Support Index to assess perceived social support through a face-to-face interview. Higher scores indicate better social support. Linear regression analysis was carried out to determine the factors that influence perceived social support by adapting the conceptual model of social support determinants and its impact on health.
RESULTS: A total of 3959 respondents aged ≥60 years completed the Duke Social Support Index. The estimated mean Duke Social Support Index score was 27.65 (95% CI 27.36-27.95). Adjusted for confounders, the factors found to be significantly associated with social support among older adults were monthly income below RM1000 (-0.8502, 95% CI -1.3523, -0.3481), being single (-0.5360, 95% CI -0.8430, -0.2290), no depression/normal (2.2801, 95% CI 1.6666-2.8937), absence of activities of daily living (0.9854, 95% CI 0.5599-1.4109) and dependency in instrumental activities of daily living (-0.3655, 95% CI -0.9811, -0.3259).
CONCLUSION: This study found that low income, being single, no depression, absence of activities of daily living and dependency in instrumental activities of daily living were important factors related to perceived social support among Malaysian older adults. Geriatr Gerontol Int 2020; 20: 63-67.