A novel mathematical approach to diagnose premenstrual syndrome

Journal of Medical Systems

Volumn 36, Issue 4, 2012, Pages 2177-2186

  Chattopadhyay, Subhagata ^a   Acharya, U Rajendra ^b  

^a NATIONAL INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

Author keywords

Borderline; Decision tree; Fuzzy model; PMS

Indexed keywords

ADULT; ANXIETY; ARTICLE; BLOATING; CLINICAL ARTICLE; COMPUTER; CONFUSION; DECISION TREE; DEPRESSION; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; FEMALE; FUZZY LOGIC; HEADACHE; HUMAN; IRRITABILITY; LIMB SWELLING; MASTALGIA; MEDICAL INFORMATION SYSTEM; PREMENSTRUAL SYNDROME; PSYCHOSOCIAL WITHDRAWAL; STATISTICAL MODEL;

DECISION TREES; FEMALE; FUZZY LOGIC; HUMANS; MODELS, STATISTICAL; PREMENSTRUAL SYNDROME;

EID: 84871535700     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9683-4     Document Type: Article

References (35)

1. Pereira-Vega, A., Sánchez, J. L., Gil, F. L., Maldonado, J. A., Bravo, J. M., Ignacio, J. M., Vázquez, R., Álvarez, F., Romero, P., and Sánchez, I., Premenstrual asthma and symptoms related to premenstrual syndrome. J. Asthma 47(8):835-40, 2010.
2. Chattopadhyay, S., Aetiology, diagnosis and management of premenstrual changes (Pmcs): Current views. Internet J. Gynec. Obst. 3 no. 1 [Serial online: last accessed 01.11.10], 2004.
3. Premenstrual syndrome. ACOG Prac. Bull. no. 15, 2000.
4. Diagnostic and statistical manual of mental disorders. American Psychiatric Association. 4th edition. American Psychiatric Association: Washington, D.C. pp. 715-8, 1994.
5. Kiesner, J., One woman's low is another woman's high: Paradoxical effects of the menstrual cycle. Psychoneuroendocrinology. 36(1):68-76, 2011.
6. García Jordá, E., Telemedicine: Shortening distances. Clin. Transl. Oncol. 12(10):650-51, 2010.
7. Moscaritolo, M., Knezevich, F. P., 3rd, Zimmer-Galler, I., Jampel, H., and Zeimer, R., A machine vision method for automated alignment of fundus imaging systems. Ophthalmic Surg Lasers Imaging 29:1-7, 2010.
8. Chattopadhyay, S., Pratihar, D. K., De Sarkar, S. C., Some studies on fuzzy clustering of psychosis data. Int. J. Business Intel Data Mining. 2 no. 2 pp. 143-59.
9. Wall, C. C., and Mann, D. A., Large scale passive acoustic data management. J. Acoust. Soc. Am. 128(4):2437, 2010.
10. Daneshgar, F., Chattopadhyay, S., A Framework for Crisis Management in Developing Countries. Int Decis Tech. 5(2): 189-199, 2011.
11. http://clinicalinformatics.stanford.edu/
12. Gangnon, R. E., A model for space-time cluster detection using spatial clusters with flexible temporal risk patterns. Stat. Med. 29(22):2325-37, 2010.
13. Chattopadhyay, S., Daneshgar F., A study on suicidal risks in psychiatric adults. Int. J. Biomed Eng. Tech. 2009 (in press).
14. Groot, K. B., Stijnen, T., Weinstein, M. C., Hunink, M. G., The combined analysis of uncertainty and patient heterogeneity in medical decision models. Med. Decis. Mak. 2010 PMID: 20974904 (in press).
15. He, Z., Cichocki, A., Xie, S., and Choi, K., Detecting the number of clusters in n-way probabilistic clustering. IEEE Trans. Pattern Anal. Mach. Intell. 32(11):2006-21, 2010.
16. Tsipouras, M. G., Exarchos, T. P., Fotiadis, D. I., Kotsia, A. P., Vakalis, K. V., Naka, K. K., and Michalis, L. K., Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Trans. Inf. Technol. Biomed. 12(4):447-58, 2008.
17. Akgundogdu, A., Kurt, S., Kilic, N., Ucan, O. N., and Akalin, N., Diagnosis of renal failure disease using adaptive neuro-fuzzy inference system. J. Med. Syst. 34(6):1003-9, 2009.
18. Chattopadhyay, S., Pratihar, D. K., and De Sarkar, S. C., Developing fuzzy classifiers to predict the chance of occurrence of adult psychoses. Knowl.-Based Syst. 20(6):479-497, 2008.
19. Khanna, S., Artificial intelligence: Contemporary applications and future compass. Int. Dent. J. 60(4):269-72, 2010.
20. Chattopadhyay,S., Davis, R. M., Menezes, D. D., Singh, G., Acharya, R. U., Tamura, T. Application of Bayesian classifier for the diagnosis of dental pain. J. Med. Syst. 2010 doi: 10.1007/s10916-010-9604-y
21. Singh,S., Kumar, A., Panneerselvam, K., Vennila, J. J., Diagnosis of arthritis through fuzzy inference system. J. Med. Syst. 2010 doi: 10.1007/s10916-010-9606-9
22. Siristatidis, C. S., Chrelias, C., Pouliakis, A., Katsimanis, E., and Kassanos, D., Artificial neural networks in gynaecological diseases: Current and potential future applications. Med. Sci. Monit. 16(10):231-6, 2010.
23. Qiu, Y., Xu, H., and Zhao, D. Y., Therapeutic evaluation on complex interventions of integrative medicine and the potential role of data mining. Chin. J. Integr. Med. 16(5):466-71, 2010.
24. De la Fuente, J. M., Bengoetxea, E., Navarro, F., Bobes, J., Alarcón, R. D., Interconnection between biological abnormalities in borderline personality disorder: Use of the Bayesian networks model. Psychiatry Res. 186(2-3):315-319, 2010.
25. Feuerstein, M., and Shaw, W. S., Measurement properties of the calendar of premenstrual experience in patients with premenstrual syndrome. J. Reprod. Med. 47(4):279-89, 2002.
26. Anderson, T. W., and Darling, D. A., Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Ann. Math. Stat 23:193-212, 1952.
27. Samantaray, S. R., El-Arroudi, K., Joós, G., and Kamwa, I., A fuzzy rule-based approach for islanding detection in distributed generation. IEEE Trans. Power Deliv. 25(3):1427-1433, 2010.
28. Han, J., Kamber, M., Data mining: Concepts and techniques, 2nd Edition. Morgan Kaufmann Publishers, ISBN 1-22860-901-6, pp. 292-06.
29. http://gautam.lis.illinois.edu/monkmiddleware/public/analytics/ decisiontree.html
30. http://www.docstoc.com/docs/20254741/WEKA-Manual-for-Version-3-6-0
31. Quinlan, J. R., Improved use of continuous Attributes in c4.5. J. Artif. Intell. Res. 4:77-90, 1996.
32. Zadeh, L. A., Fuzzy algorithms. Inf. Control 12(2):94-102, 1968.
33. www.minitab.com
34. Cowdry, R. W., Gardner, D. L., O'Leary, K. M., Leibenluft, E., and Rubinow, D. R., Mood variability: A study of four groups. Am. J. Psychiatry 148(11):1505-11, 1991.
35. Zukov, I., Ptácek, R., Raboch, J., Domluvilová, D., Kuzelová, H., Fischer, S., and Kozelek, P., Premenstrual dysphoric disorder-review of actual findings about mental disorders related to menstrual cycle and possibilities of their therapy. Prague Med. Rep. 111(1):12-24, 2010.