Mental illness is a set of health problems that affect the way individuals perceive themselves, relate to others, and interact with the world around them. Due to the myriad of underlying causes and subsequent effects of mental illness, these conditions often trigger fear and misunderstanding among the general population. Common mental illnesses such as depression and anxiety disorders often affect an individual's thoughts, feelings, abilities, and behaviours. Anxiety disorder is characterized by an irrational fear of certain things or events. It is often attributed as the feeling of worry about anticipated events and fear in response to current events. This work has identified several related research efforts on the general well-being and psychological distress using data mining. However, there is inadequate research done using a similar method on specific mental health issues, especially related to generalized anxiety disorder (GAD). In view of this gap, this study focuses on implementing a novel feature selection and data mining classifier system. Under the proposed method, Shapley value will be implemented as the feature selection of the data mining classifier on the mental health data. The approach is used to predict GAD among women. The methodology for this research is adapted from the process of Knowledge Discovery in Databases (KDD). This methodology consists of 5 main phases; namely data acquisition, data pre-processing, feature selection, classification prediction, and evaluation. Using this enhanced prediction algorithm, any women can get help if they are perceived to be suffering from GAD. By designing an effective way of identifying individuals who may be suffering from mental illnesses, we hope that our work would improve the awareness surrounding mental health issues especially among women and enable them to undertake autonomous decision in seeking mental health services.
Skin grafts are indicated when there is a major loss of skin. Full-thickness skin graft is an ideal choice to reconstruct defect of irregular surface that is difficult to immobilize. Full-thickness mesh grafts can be applied to patch large skin defect when there is less donor site in extensively traumatized and burned surgical patients. The concept of using natural biomaterials such as keratin, basic fibroblast growth factor is slowly gaining popularity in the field of medical research to achieve early healing. The main objective of this study is to evaluate the efficacy of gelatin conjoined with keratin processed from the poultry feather and commercially available basic fibroblast growth factor (bFGF) as a sandwich layer in promoting the viability of full-thickness skin mesh grafts. The efficacy was assessed from the observation of clinical, bacteriological, and histopathological findings in three groups of experimental dogs. The clinical observations such as color, appearance and discharge, and hair growth were selected as criteria which indicated good and early acceptance of graft in keratin-gelatin (group II). On bacteriological examination, Staphylococcus aureus and Proteus was identified in few animals. Histopathological study of the patched graft revealed early presences of hair follicles; sebaceous gland, and normal thickness of the epidermis in keratin-gelatin in group II treated animals compared with other group (group I-control, group III-bFGF-gelatin).