METHODS: This cross-sectional study was performed in two Malaysian health clinics by using the Malay version of a self-administered questionnaire. This instrument contains a diabetes care profile, a 21-item version of the Depression Anxiety Stress Scales (DASS21), and a Malaysian Medication Adherence Score (MalMAS). Simple and multiple logistic regression analyses were performed.

RESULTS: A total of 338 type II diabetes mellitus patients responded (response rate 93.1%). The proportion of patients with poor glycaemic control was 76.0%. Multiple logistic regression analysis showed that 1) social support scores [Adj. OR (95% CI): 1.06 (1.03,1.10); p = 0.001]; 2) unemployment [Adj. OR (95% CI): 0.46 (0.22,0.95); p = 0.035]; 3) pensioner status [Adj. OR (95% CI): 0.28 (0.13,0.61); p = 0.001]; and 4) perception of diabetes as interfering with daily living activities [Adj. OR (95% CI): 3.18 (1.17,8.70); p = 0.024] were significant factors for poor glycaemic control.

PATIENTS AND METHODS: The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study.

RESULTS: With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression.

METHODS: This is a longitudinal cohort study. Psoriatic arthritis (PsA) patients with liver enzymes abnormalities were identified. Our control group consisted of PsA patient from the same cohort who had no history of liver abnormalities. Factors associated with liver abnormalities were identified using univariate and multivariate analysis.

RESULTS: A total of 247 of PsA patients were included and out of those, 99 developed liver enzymes abnormalities. The mean age of the patients was 56 years old (±13.5) with 56.1% female and 39.4% Indian descendants. The univariate logistic regression demonstrated that disease duration of PsA (OR=1.06, 95% CI=1.01 - 1.10, p=0.012), diabetes mellitus (OR=2.16, 95% CI=1.26 - 3.70, 0.005) and non-alcoholic fatty liver disease (NAFLD) (OR=3.90, 95% CI = 1.44 - 10.53, p=0.007) were associated with abnormal liver function in PsA patients. No association was found with both conventional synthetic disease-modifying antirheumatic drugs or biologics.