ROLE OF KINESIOPHOBIA ON PAIN, DISABILITY AND QUALITY OF LIFE IN PATIENTS WITH CHRONIC LOW BACK PAIN: A SYSTEMATIC REVIEW

Background and Aim: The majority of people suffered with low back pain (LBP) at least once during their lifetime. As such, LBP is a highly prevalent and costly condition. People respond inappropriately as a result of current or possible risks and establish defensive habits (for example, hyper-vigilance) that aim at avoiding new injuries. A continued reconciling of studies which provide various answers for the same issue will be necessary for treatment decisions. This study is performed to conclude the function of Kinesiophobia and check it on   Pain, Disability and Quality of Life in Patients that are suffering from Chronic Low Back Pain: A Systematic Review. Methodology: A Systematic Review has been conducted. Secondary data collected from Electronic database including PubMed, Medline and Cochrain Library from inception to 2010. Total 554 Article found out of which 10 articles included in the study after excluding the duplicate article, Quality screening through Pedro Scale, and article don’t fulfilling the inclusion criteria of the study. Review completed within 9 months after approval of synopsis. Results: According to this Review total Sample size was 554 with mean Sample size 130±90, mean Age 46±5 years, Mean of Pain Intensity (VAS 0-10) 6.12±1.5, mean Pain Duration 30±14, mean Kinesiophobia Measures (Tampa Scale of Kinesiophobia 0-68) 37±6.5, mean Disability (Oswestry Disability Index 0-100%) 56±27, mean Quality of Life (SF 36 0-100) 39.17±15.197.  Conclusion: TSK scores showed a statistically significant correlation with Pain, Disability, education level, and SF-36 QOL. As the education level decreases, kinesiophobia scores increase and as kinesiophobia scores increase, Level of disability increases and the quality of life decreases. Patients with kinesiophobia presented greater pain intensity, a greater fear of movement and of performing physical activities and it was also associated with worse quality of life.

Hierarchical Multi-Label Document Classification Using Swarm Intelligence

Document classification is one of the important fields of text mining. At present, category identification using taxonomy for scientific publications is a manual task. These taxonomies support authors which contain a large number of classes organized in the form of hierarchy that becomes quite difficult to choose a relevant category or categories for their work. Due to the amalgamation of research work in multiple domains, the problem becomes a multi-label classification (MLC). MLC is broadly solved using two different approaches (Problem Transformation and algorithm adaptation). In literature, a lot of single label classifiers are available to deal with single label dataset such as Support Vector Machine (SVM), K Nearest Neighbour (KNN), Naive Bayes and many more, these classifiers have low accuracy on text datasets due to the similarity measures and inappropriate selection of features. Similar approaches exist, which transform the multi label dataset into binary classification problems such as Binary Relevance (BR), Classifier Chain (CC), Probabilistic Classifier Chain (PCC) and many more. These algorithms also have a very low accuracy for text data. The issue which has not given proper importance is the order in which the binary classifiers are executed. Algorithm adaptation techniques such as decision trees, SVM, Multi-label K Nearest Neighbour (ML-KNN) and neural network also exist for MLC but have low accuracy due to similar weightage of features for all labels and have never been tested for a scientific publication datasets. The algorithm adaptation approaches have never been studied with feature weighting as all the features may not play the same role for each label in the MLC. We argue that all the approaches to deal with MLC for scientific documents suffer from low accuracy.