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Background: Many published studies in Malaysia have examined and assessed self care among type 2 diabetes mellitus (T2DM) patients using the Summary of Diabetes Self Care Activities (SDSCA) scale. The current paper is a meta-analysis of related studies that also examines how gender and ethnicity influence and shape T2DM self care practices in Malaysia. Methods: We undertook a bibliographic search for studies conducted and published in Malaysia on T2DM adults using the SDSCA scale. This is a two-stage individual participant meta-analysis of SDSCA which synthesised the overall and subscale score based on gender and ethnic groups as well as the correlation between SDSCA and HbA1c. Results: We examined 11 studies that utilised SDSCA to analyse 3,720 T2DM patients. The overall SDSCA score was 33.46 (47.8% of the 7-day week). The subscale score for general diet, specific diet, exercise, blood glucose self-monitoring and foot care were 4.80, 4.09, 2.87, 1.80 and 3.21, respectively. A small but statistically significant better self care in some gender or ethnic groups was noted. The SDSCA diet subscale and HbA1c showed statistically significant correlation. Conclusion: The finding suggested Malaysian T2DM patients were deficient in exercise and blood glucose self-monitoring. In fact, overall self care among Malaysian adult T2DM patients appears to be suboptimal across gender and the three main ethnic groups. Greater efforts are therefore needed to educate Malaysian adult T2DM patients to improve their self care practices. [ABSTRACT FROM AUTHOR]

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts. [ABSTRACT FROM AUTHOR]