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Effective Classification of Major Depressive Disorder Patients Using Machine Learning Techniques

机译：使用机器学习技术有效分类主要抑郁症患者

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Background: Major Depressive Disorder (MDD) in simple terms is a psychiatric disorderwhich may be indicated by having mood disturbances which are consistent for more than a fewweeks. It is considered a serious threat to psychophysiology which when left undiagnosed may evenlead to the death of the victim so it is more important to have an effective predictive model. Themajor Depressive disorder is often termed as comorbid medical condition (medical condition thatco-occurs with another), it is hardly possible for the physicians to predict that the victim is under depression,timely diagnosis of MDD may help in avoiding other comorbidities. Machine learning is abranch of artificial intelligence which makes the system capable of learning from the past and withthat experience improves the future results even without programming explicitly. As in recent daysbecause of the high dimensionality of features, the accuracy of the predictions is comparatively low.In order to get rid of redundant and unrelated features from the data and improve the accuracy, relevantfeatures must be selected using effective feature selection methods.Objective: This study aims to develop a predictive model for diagnosing the Major Depressive Disorderamong the IT professionals by reducing the feature dimension using feature selection techniquesand evaluate them by implementing three machine learning classifiers such as Na?ve Bayes,Support Vector Machines and Decision Tree.Method: We have used Random Forest based Recursive Feature Elimination technique to reduce thefeature dimensions.Results: The results show a considerable increase in prediction accuracy after applying feature selectiontechnique.Conclusion: From the results, it is implied that the classification algorithms perform better after reducingthe feature dimensions.
机译：背景：在简单的术语中，主要抑郁症（MDD）是一种精神病疾病，可以通过具有多个以上的多周的情绪干扰来表示。它被认为是对心理生理学的严重威胁，当左侧未确诊可能甚至可能甚至可能导致受害者的死亡，因此具有有效的预测模型更为重要。 HOMAJOR抑郁症通常被称为同伴的医疗状况（医疗状况，通过另一个人发生的情况），医生几乎不能预测受害者受到抑郁症，及时诊断MDD可能有助于避免其他合并症。机器学习是人工智能的全力，使得能够从过去学习的系统，并且在没有明确编程的情况下也可以提高未来的结果。与最近的特征的高度一样，预测的准确性相对较低。在从数据中摆脱冗余和不相关的功能并提高准确性，必须使用有效的特征选择方法选择相关的优点.bjective：本研究旨在通过使用特征选择技术减少特征维度来开发诊断主要抑郁紊乱的预测模型，通过特征选择技术来通过实施三种机器学习分类器（如Na've Bayes，支持向量机和决策树）评估它们。我们使用随机林的递归特征消除技术来减少Feature尺寸。结果：结果显示在应用特征选择后的预测精度的相当大的提高。结论：从结果中暗示，在减少特征尺寸后，暗示分类算法更好地执行更好。

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