METHODS: A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.

RESULTS: In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.

MATERIALS AND METHODS: A cross-sectional study was carried out among 788 participants in three KYL health campaigns from 2017 to 2020. Perceived knowledge (a 5-item Likert scale was used, zero means "very poor" and 4 means "excellent knowledge") and confidence in identifying BC symptoms were studied. A Wilcoxon Matched-Paired Signed-Rank Test was performed to assess the perceived knowledge.

RESULTS: There was a significant improvement in the perceived knowledge Mean (±SD) score (2.84 ± 1.02) versus (4.31 ± 0.66) before and after the campaign (P < 0.01). About 95.6% agreed that the language used in KYL materials was clear and understandable, 89.8% agreed it is acceptable in Malaysian culture, and 80% felt more confident in identifying BC symptoms. Therefore, 90.8% had the intention of breast self-examination and 90.8% would consult a doctor if symptomatic. The majority (92.7%) agreed that the KYL tools clarified the BC tests needed.

METHODS: This is a cross-sectional study involving a convenient sampling of 258 undergraduate students. Self-administered structured questionnaires adapted from the Depression, Anxiety and Stress Scale-21 (DASS-21) and the Fear of COVID-19 Scale (FCV-19S), were used to assess the severity of psychological symptoms (depression, anxiety and stress) and fear. The perception towards ODL is also designed to assess the students' perception related to ODL implementation. The data were analysed using descriptive statistics and Structural Equation Modelling-Partial Least Square (SEM-PLS).

RESULTS: Overall, 84.2%, 95.0% and 67.4% of the participants experienced moderate to very severe level of depression, anxiety and stress, respectively. In addition, 82.6% of them suffering with moderate to extreme level of fear, of which 81.8% of participants had a negative view on ODL. The results of SEM-PLS revealed that there are complementary partial mediation effects of fear on the relationship between depression and students' perception during ODL (β = 0.502, t-value = 0.828, P-value = 0.017). The anxiety (β = 0.353, t-value = 5.401, P-value = 0.000) and stress (β = 0.542, t-value = 8.433, P-value = 0.000) have directly influenced on fear.