METHODS: Using a subset of survey data from the National Health and Morbidity Survey (NHMS) 2019, a secondary data analysis was performed. Trained research assistants collected data through face-to-face method using a mobile tablet questionnaire system application. Logistic regression analysis was performed to examine the relationship between sociodemographic factors, physical activity, and cervical cancer screening. The analyses were conducted using STATA version 14 (Stata Corp, College Station, Texas, USA), accounting for sample weighs and complex sampling design.
RESULTS: The analysis included 5,650 female respondents, representing an estimated 10.3 million Malaysian female adults aged 18 and above. Overall, 35.2% (95%CI 33.2, 37.4) respondents had a Pap smear test within the past three years. Respondents who were physically active were 1.41 times more likely to have a Pap smear test. Similarly, respondents aged 35-59 (OR 1.84; 95%CI 1.46, 2.34) and those living in rural localities (OR 1.38; 95%CI 1.13, 1.70) had higher odds of receiving a Pap smear test. Compared to married respondents, single respondents (OR 0.04; 95%CI 0.02, 0.07) and widowed/divorcee respondents (OR 0.72; 95%CI 0.56, 0.82) were less likely to receive a Pap smear test. Educated respondents were more likely to have had a Pap smear test.
CONCLUSIONS: The overall prevalence of cervical cancer screening in Malaysia remains low (35.2%). Efforts should be made to strengthen health promotion programs and policies in increasing awareness on the significance of cervical cancer screening. These initiatives should specifically target younger women, single women, and widowed/divorced individuals. The higher cervical screening uptake among rural women should be studied further, and the enabling factors in the rural setup should be emulated in urban areas whenever possible.
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.
CONCLUSION: These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.