METHODOLOGY: We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC.

RESULTS: We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods.

METHODS: We examined and included 13 documents for the presence or lack of a statement of intent and/or actions related to caring for women at risk for or experiencing PND.

RESULTS: Although PND is actively researched and included in the clinical practice guidelines, no other policy documents mention PND.

METHODS: A household-based cross-sectional study was conducted in March 2024 in six Semai sub-ethnic indigenous villages in Pos Lenjang, Kuala Lipis, Pahang. A structured questionnaire was administered to randomly selected individuals (≥ 12 years old) to collect data on sociodemographic characteristics and KAP. Data were analysed using descriptive statistics and predictors of KAP were determined using logistic regression. A p-value less than 0.05 was considered statistically significant.