SSN College of Engineering, Chennai, India.
J Med Syst. 2018 Apr 3;42(5):88. doi: 10.1007/s10916-018-0934-5.
Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.
心理健康是个体情感、心理和社会福祉的一个指标。它决定了一个人如何思考、感受和应对各种情况。积极的心理健康有助于一个人高效地工作并充分发挥自己的潜力。心理健康在人生的各个阶段都很重要，从儿童和青少年到成年。许多因素都会导致心理健康问题，从而引发精神疾病，如压力、社交焦虑、抑郁、强迫症、药物成瘾和人格障碍。确定精神疾病的发病时间以保持适当的生活平衡变得越来越重要。机器学习算法和人工智能 (AI) 的性质可以充分应用于预测精神疾病的发病。此类应用程序如果实时实施，将通过作为具有异常行为的个人的监测工具，为社会带来益处。这项研究工作旨在应用各种机器学习算法，如支持向量机、决策树、朴素贝叶斯分类器、K-最近邻分类器和逻辑回归，以识别目标群体的心理健康状况。首先，对目标群体对设计问卷的回答进行无监督学习技术处理。通过计算平均意见得分来验证聚类结果的标签。然后，使用这些聚类标签来构建分类器来预测个人的心理健康状况。研究考虑了来自不同群体的人群，如高中生、大学生和工作专业人员作为目标群体。研究分析了将上述机器学习算法应用于目标群体的情况，并为未来的工作提出了建议。
