Computer Science Department, Prince Sattam Bin Abdulaziz University, Aflaj, Saudi Arabia.
Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakaka, Saudi Arabia.
Comput Intell Neurosci. 2022 Jun 28;2022:1332664. doi: 10.1155/2022/1332664. eCollection 2022.
Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.
双相情感障碍的特点是情绪在躁狂和抑郁之间波动。作为最常见的精神疾病之一，双相情感障碍（BD）的阶段经常被误诊为重度抑郁症（MDD），导致治疗效果不佳和预后不良。因此，在疾病的早期阶段区分 MDD 和 BD 可能有助于更有效和有针对性的治疗。在这项研究中，采用了一种受蚁群优化（ACO）启发并遵循所需 ACO 的改进 ACO（IACO）技术，通过删除不相关或冗余的特征数据来最小化特征数量。为了区分 MDD 和 BD 个体，选择的特征被加载到支持向量机（SVM）中，这是一种用于分类过程、回归、功能估计和建模操作的复杂数学技术。在考虑分类效率和提取特征的频率方面，IACO 方法的性能与常规 ACO、粒子群优化（PSO）和遗传算法（GA）技术相关联。通过使用嵌套交叉验证（CV）方法进行验证，产生了几乎可靠的分类错误估计。
