A computational approach for correlational analysis of symptoms of major depressive disorder
DOI:
https://doi.org/10.53992/njns.v10i3.272Keywords:
Machine Learning Algorithms, Major Depressive Disorder (MDD), Association Rule, Decision Tree, Agglomerative ClusteringAbstract
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.
References
Sandmire HF, Austin SD, Bechtel RC. Experience with 40,000 Papanicolaou smears. Obstetrics and Gynecology. 1976 Jul 1;48(1):56-60.
Li Z, Ruan M, Chen J, Fang Y. Major depressive disorder: advances in neuroscience research and translational applications. Neuroscience bulletin. 2021 Jun;37(6):863-880.
M. Malgaroli, A. Calderon, and G. A. Bonanno, “Networks of major depressive disorder: A systematic review,” Clin. Psychol. Rev., vol. 85, no. July 2020, p. 102000, 2021, doi: 10.1016/j.cpr.2021.102000.
Li Y, Gui Y, Zhao M, Chen X, Li H, Tian C, Zhao H, Jiang C, Xu P, Zhang S, Ye S. The roles of extracellular vesicles in major depressive disorder. Frontiers in psychiatry. 2023 Mar 9;14:1138110.
Yun M, Jeon M, Yang H. A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data. Plos one. 2024 May 22;19(5):e0303889.
Muñoz RA, McBride ME, Brnabic AJ, López CJ, Hetem LA, Secin R, Dueñas HJ. Major depressive disorder in Latin America: the relationship between depression severity, painful somatic symptoms, and quality of life. Journal of affective disorders. 2005 May 1;86(1):93-98.
Arnaud AM, Brister TS, Duckworth K, Foxworth P, Fulwider T, Suthoff ED, Werneburg B, Aleksanderek I, Reinhart ML. Impact of major depressive disorder on comorbidities: a systematic literature review. The Journal of clinical psychiatry. 2022 Oct 19;83(6):43390.
Tekkeşin Aİ. Artificial intelligence in healthcare: past, present and future. Anatol J Cardiol. 2019 Oct 1;22(Suppl 2):8-9.
Kumar S, Chong I. Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states. International journal of environmental research and public health. 2018 Dec;15(12):2907.
Rai VK, Chakraborty S, Chakraborty S. Association rule mining for prediction of COVID-19. Decision Making: Applications in Management and Engineering. 2023 Apr 8;6(1):365-378.
Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. Journal of applied science and technology trends. 2021 Mar 24;2(01):20-28.
Barile S, Bassano C, Lettieri M, Piciocchi P, Saviano M. Intelligence augmentation (IA) in complex decision making: a new view of the VSA concept of relevance. InInternational Conference on Applied Human Factors and Ergonomics. Cham: Springer International Publishing. 2020 Jul 10; 251-258.
Rauf A, Ammar M, Azhar M, Noor N, Bakhtiar SM. Unveiling the Secrets of Obesity with Machine Learning (ML) Techniques/Algorithms. Current Trends in OMICS. 2024;4(1):55-76.
Tokuda EK, Comin CH, Costa LD. Revisiting agglomerative clustering. Physica A: Statistical mechanics and its applications. 2022 Jan 1;585:126433.
Colledani D, Anselmi P, Robusto E. Machine learning-decision tree classifiers in psychiatric assessment: An application to the diagnosis of major depressive disorder. Psychiatry Research. 2023 Apr 1;322:115127.
Cui L, Li S, Wang S, Wu X, Liu Y, Yu W, Wang Y, Tang Y, Xia M, Li B. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal transduction and targeted therapy. 2024 Feb 9;9(1):30.
Krogh J, Benros ME, Jørgensen MB, Vesterager L, Elfving B, Nordentoft M. The association between depressive symptoms, cognitive function, and inflammation in major depression. Brain, behavior, and immunity. 2014 Jan 1;35:70-76.
Kost R, Littenberg B, Chen ES. Exploring generalized association rule mining for disease co-occurrences. InAMIA Annual Symposium Proceedings 2012;Vol. 2012: 1284.
Corponi F, Anmella G, Pacchiarotti I,
Samalin L, Verdolini N, Popovic D, Azorin JM, Angst J, Bowden CL, Mosolov S, Young AH. Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study. Translational psychiatry. 2020 Jul 19;10(1):241.
Holdaway AS, Luebbe AM, Becker SP. Rumination in relation to suicide risk, ideation, and attempts: Exacerbation by poor sleep quality. Journal of affective disorders. 2018 Aug 15; 236:6-13
Christensen MC, Wong CM, Baune BT. Symptoms of major depressive disorder and their impact on psychosocial functioning in the different phases of the disease: do the perspectives of patients and healthcare providers differ?. Frontiers in Psychiatry. 2020 Apr 24;11:280.
Zhou J, Zhou J, Feng L, Feng Y, XiaoL, Chen X, Yang J, Wang G. The
associations between depressive symptoms, functional impairment, and quality of life, in patients with major depression: undirected and Bayesian network analyses. Psychological Medicine. 2023 Oct;53(14):6446-6458.
Weijers A, Rasing S, Creemers D, Vermulst A, Schellekens AF, Westerhof GJ. The relationship between depressive symptoms, general psychopathology, and well‐being in patients with major depressive disorder. Journal of Clinical Psychology. 2021 Jun;77(6):1472-1486.
Lee KS, Ham BJ. Machine learning on early diagnosis of depression. Psychiatry Investigation. 2022 Aug 24;19(8):597.
Lam RW, Kennedy SH, McIntyre RS, Khullar A. Cognitive dysfunction in major depressive disorder: effects on psychosocial functioning and implications for treatment. The Canadian Journal of Psychiatry. 2014 Dec;59(12):649-654.
