A computational approach for correlational analysis of symptoms of major depressive disorder Article Swipe
Abdur Rauf
,
Zeenat Fatima
,
Sandip Shah
,
Syeda Marriam Bakhtiar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.53992/njns.v10i3.272
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.53992/njns.v10i3.272
Major Depressive Disorder (MDD) is a complicated mental illness that consists of wide range of correlated symptoms. According to DSM-V, it is characterized by pervasive low mood, loss of interest or pleasure in nearly all activities, and additional symptoms that can cause significant distress in social, occupational, and important areas of life. In this study, association rules, decision tree classification and agglomerative clustering are employed to classify MDD symptoms interconnection and their co-occurrence pattern. A combination of key symptoms occurrence, such as aggression with euphoric responses and overthinking with euphoria are identified through association rules that show highest lift values. Decision tree is employed to predict primary node which is mood swing as key predictor of MDD. Then, agglomerative clustering is used to split the dataset into three clusters based on expert diagnosis to identify the range of symptoms that overlap. In this study, computational approach is utilized to unravel the hidden pattern and overlapping of correlated symptoms that will help in improved diagnosis and better personalized treatment plans. This study highlighted the interrelationships of symptoms associated with MDD and their thorough examination for therapeutic approach. Future perspectives should focus on diverse datasets with extension and validation of findings that produce sustainable findings for clinical decision making. By the complex interplay of symptoms of MDD shows the contribution of this research towards advancement of evidence-based diagnostics, ultimately aim to improve clinical outcomes by intergradational symptomatic study.
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- https://doi.org/10.53992/njns.v10i3.272
- https://njns.nust.edu.pk/index.php/njns/article/download/272/184
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All OpenAlex metadata
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A computational approach for correlational analysis of symptoms of major depressive disorderWork title
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articleOpenAlex work type
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2025Year of publication
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2025-11-18Full publication date if available
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Abdur Rauf, Zeenat Fatima, Sandip Shah, Syeda Marriam BakhtiarList of authors in order
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https://doi.org/10.53992/njns.v10i3.272Publisher landing page
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https://njns.nust.edu.pk/index.php/njns/article/download/272/184Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://njns.nust.edu.pk/index.php/njns/article/download/272/184Direct OA link when available
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0Total citation count in OpenAlex
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