Department of CS, AIMT, Ambala, India.
Department of CSA, DAV University, Jalandhar.
Psychiatr Danub. 2023 Fall;35(3):355-368. doi: 10.24869/psyd.2023.355.
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.
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.
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%.
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.
焦虑和抑郁是两种主要的人类心理障碍。在这项工作中，我们使用了几种基于群体智能的启发式元算法技术，以找到这两种人类心理障碍的最佳特征集。
为了诊断人们的抑郁和焦虑，我们考虑并检查了一个由 1128 个实例和 46 个属性组成的随机数据集。该数据集是通过访问位于印度哈里亚纳邦（印度的一个邦）不同城市的许多诊所收集和手动编制的。之后，我们使用了九种新兴的元启发式技术（遗传算法、二进制灰狼优化器、蚁群优化算法、粒子群优化算法、人工蜂群算法、萤火虫算法、蜻蜓算法、蝙蝠算法和鲸鱼优化算法）来找到用于诊断人类抑郁和焦虑的最佳特征集。为了避免局部最优和平衡探索和开发，我们设计了一种新的混合特征选择技术，称为基于受限交叉变异的鲸鱼优化算法（RCM-WOA）。
基于群体智能的启发式元算法已应用于数据集。这些算法的性能使用不同的性能指标进行评估，如准确性、敏感性、特异性、精度、召回率、F1 度量、错误率、执行时间和收敛曲线。利用提出的方法 RCM-WOA 达到的准确率为 91.4%。
抑郁和焦虑是两种严重的心理障碍，可能导致其他慢性和危及生命的人类疾病。与其他最先进的方法相比，提出的算法（RCM-WOA）被发现更适用。
