Breast cancer survival prediction can have
an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict
the survival prospects of patients, but newer algorithms such as deep learning can be tested with the
aim of improving the models and prediction accuracy. In this study, we used machine learning and deep
learning approaches to predict breast cancer survival in 4,902 patient records from the University of
Malaya Medical Centre Breast Cancer Registry. The
results indicated that the multilayer perceptron (MLP),
random forest (RF) and decision tree (DT) classifiers
could predict survivorship, respectively, with 88.2 %,
83.3 % and 82.5 % accuracy in the tested samples.
Support vector machine (SVM) came out to be lower
with 80.5 %. In this study, tumour size turned out to
be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction
accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.
Maternal effects of heat shock are reported for some species of insects, but little is known about such effects in the western flower thrips (WFT) Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae). WFT is a pest of vegetables in greenhouses worldwide. It is susceptible to high temperatures in its natural environment and is controlled using heat treatment in China. WFT population growth is suppressed by a brief exposure to a high temperature of 40°C or 45°C in the laboratory. To explore the mechanism by which high temperatures suppress the growth of WFT populations, as well as the effects of multiple heat treatments on WFT, we recorded the duration of development and survival of immature WFT, and the sex ratio (female/male) and fecundity of F1, F2, F3 and F4 adult females that developed after a single heat shock, and those of F2 offspring after a double heat shock. We also recorded the longevity and ovarian structure of adult females of the treated generation (P) and their F1, F2 and F3 offspring after a single heat shock. In addition, we determined whether the effects of a heat shock on second instar nymphs and adults differed. The results indicate that exposure of the parental generation to 41°C or 45°C for 2 h significantly prolonged the duration of development, reduced survival of immature WFT and altered the sex ratio (female/male), longevity and fertility of their adult female offspring. The effects of a heat shock of 41°C persisted for two generations, whilst the effect of heat shock of 45°C persisted for three generations. In addition, double heat shocks had more pronounced effects than a single heat shock. Heat shock administered to second instar nymphs resulted in a decrease in the number of ovarioles, whilst a heat shock administered to adults resulted in ovariole deformity. The maternal effects of heat shock in terms of the biological parameters of WFT, structure and number of ovarioles, are critical in determining the suppression of the growth at high temperatures of WFT populations.