A NOVEL AND HYBRID WHALE OPTIMIZATION WITH RESTRICTED CROSSOVER AND MUTATION BASED FEATURE SELECTION METHOD FOR ANXIETY AND DEPRESSION.

BACKGROUND: Anxiety and depression are two leading human psychological disorders. In this work, several swarm intelligence-based metaheuristic techniques have been employed to find an optimal feature set for the diagnosis of these two human psychological disorders. SUBJECTS AND METHODS: To diagnose depression and anxiety among people, a random dataset comprising 1128 instances and 46 attributes has been considered and examined. The dataset was collected and compiled manually by visiting the number of clinics situated in different cities of Haryana (one of the states of India). Afterwards, nine emerging meta-heuristic techniques (Genetic algorithm, binary Grey Wolf Optimizer, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Dragonfly Algorithm, Bat Algorithm and Whale Optimization Algorithm) have been employed to find the optimal feature set used to diagnose depression and anxiety among humans. To avoid local optima and to maintain the balance between exploration and exploitation, a new hybrid feature selection technique called Restricted Crossover Mutation based Whale Optimization Algorithm (RCM-WOA) has been designed. RESULTS: The swarm intelligence-based meta-heuristic algorithms have been applied to the datasets. The performance of these algorithms has been evaluated using different performance metrics such as accuracy, sensitivity, specificity, precision, recall, f-measure, error rate, execution time and convergence curve. The rate of accuracy reached utilizing the proposed method RCM-WOA is 91.4%. CONCLUSION: Depression and Anxiety are two critical psychological disorders that may lead to other chronic and life-threatening human disorders. The proposed algorithm (RCM-WOA) was found to be more suitable compared to the other state of art methods.

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the number of psychological disorders patients has been significantly raised. This paper presents a comprehensive review of some of the major human psychological disorders (stress, depression, autism, anxiety, Attention-deficit hyperactivity disorder (ADHD), Alzheimer, Parkinson, insomnia, schizophrenia and mood disorder) mined using different supervised and nature-inspired computing techniques. A systematic review methodology based on three-dimensional search space i.e. disease diagnosis, psychological disorders and classification techniques has been employed. This study reviews the discipline, models, and methodologies used to diagnose different psychological disorders. Initially, different types of human psychological disorders along with their biological and behavioural symptoms have been presented. The racial effects on these human disorders have been briefly explored. The morbidity rate of psychological disordered Indian patients has also been depicted. The significance of using different supervised learning and nature-inspired computing techniques in the diagnosis of different psychological disorders has been extensively examined and the publication trend of the related articles has also been comprehensively accessed. The brief details of the datasets used in mining these human disorders have also been shown. In addition, the effect of using feature selection on the predictive rate of accuracy of these human disorders is also presented in this study. Finally, the research gaps have been identified that witnessed that there is a full scope for diagnosis of mania, insomnia, mood disorder using emerging nature-inspired computing techniques. Moreover, there is a need to explore the use of a binary or chaotic variant of different nature-inspired computing techniques in the diagnosis of different human psychological disorders. This study will serve as a roadmap to guide the researchers who want to pursue their research work in the mining of different psychological disorders.

Hemi-diaphragmatic paresis following extrafascial versus conventional intrafascial approach for interscalene brachial plexus block: A double-blind randomised, controlled trial.

BACKGROUND AND AIMS: Hemidiaphragmatic paresis occurs in almost all patients undergoing interscalene block for proximal upper limb surgeries. This study tested hypothesis that ultrasound-guided extrafascial approach of interscalene block under nerve stimulator guidance reduces incidence of hemidiaphragmatic paresis in comparison to intrafascial approach by achieving same degree of anaesthesia and analgesia. METHODS: Sixty patients undergoing proximal upper limb surgeries were randomised to receive an ultrasound-guided interscalene brachial plexus block (ISB) with the aid of nerve stimulator for surgical anaesthesia and analgesia using 20 mL 0.5% ropivacaine by extrafascial (Group E) or intrafascial (Group I) approach. The incidence of hemidiaphragmatic paresis was measured by M-mode ultrasound before and 30 min after the procedure. Secondary outcomes such as respiratory functions (forced vital capacity, forced expiratory volume in 1 s and peak expiratory flow rate) were measured, and complications were recorded and compared. The statistics was obtained using SPSS Version 19. Levene's test and paired and unpaired t-test were used. P value <0.05 was considered significant. RESULTS: The incidence of hemidiaphragmatic paresis was 17% and 46% in Group E and Group I, respectively (P < 0.0001). All other respiratory outcomes were preserved in Group E compared with Group I. CONCLUSION: Ultrasound-guided ISB with the aid of nerve stimulator through extrafascial approach reduces the incidence of hemidiaphragmatic paresis and also reduces respiratory function impairment when compared with intrafascial approach.