A clustering approach for autistic trait classification.

Machine learning (ML) techniques can be utilized by physicians, clinicians, as well as other users, to discover Autism Spectrum Disorder (ASD) symptoms based on historical cases and controls to enhance autism screening efficiency and accuracy. The aim of this study is to improve the performance of detecting ASD traits by reducing data dimensionality and eliminating redundancy in the autism dataset. To achieve this, a new semi-supervised ML framework approach called Clustering-based Autistic Trait Classification (CATC) is proposed that uses a clustering technique and that validates classifiers using classification techniques. The proposed method identifies potential autism cases based on their similarity traits as opposed to a scoring function used by many ASD screening tools. Empirical results on different datasets involving children, adolescents, and adults were verified and compared to other common machine learning classification techniques. The results showed that CATC offers classifiers with higher predictive accuracy, sensitivity, and specificity rates than those of other intelligent classification approaches such as Artificial Neural Network (ANN), Random Forest, Random Trees, and Rule Induction. These classifiers are useful as they are exploited by diagnosticians and other stakeholders involved in ASD screening.

Bioethics: a look at animal testing in medicine and cosmetics in the UK.

Using animals for cosmetics and medical tests has contributed towards a debate based on conflicting interests. Despite the efforts in justifying the value of animals in conducting analyses, this study seeks to elaborate whether or not it is rational to use animals as test subjects in medical and cosmetics fields. The value of animal life is at the core of the emotional conflicts that arise when animals become experimental subjects in medical and cosmetics fields. The aim of this study is to determine if there are ethical differences in the use of animal testing in medicine versus cosmetics. The research, through review and content analysis of the existing literature, compares and provides the outcomes of using animals in medical and cosmetics tests by examining studies conducted in the UK. The findings of this research indicated that animal testing is considered acceptable in the medical field only if there are no other alternatives, but is completely unacceptable in the cosmetics field. The study also provides recommendations in the form of alternatives that protect animals from cruelty and may benefit the different stakeholders and the society at large.