Methods: A child-sized mannequin head was developed to measure light illuminance levels with and without sun-protective equipment, across a wide range of environments in Singapore, outdoors (open park, under a tree, street) and indoors (under a fluorescent illumination with window, under white LED-based lighting without window). A comparison was made between indoor and outdoor light levels that are experienced while children are involved in day-to-day activities.
Results: Outdoor light levels were much higher (11,080-18,176 lux) than indoors (112-156 lux). The higher lux levels protective of myopia (>1000 lux) were measured at the tree shade (5556-7876 lux) and with hat (4112-8156 lux). Sunglasses showed lux levels between 1792 and 6800 lux. Although with sunglasses readings were lower than tree shade and hat, light levels were still 11 to 43 times higher than indoors.
Conclusions: Recommendations on spending time outdoors for myopia prevention with adequate sun protection should be provided while partaking in outdoor activities, including protection under shaded areas, wearing a hat or sunglasses, sunscreen, and adequate hydration.
Translational Relevance: Light levels outdoors were higher than indoors and above the threshold illuminance for myopia prevention even with adequate sun-protective measures.
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.
CONCLUSION: Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.