Refine your search
Collections
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Mondal, Abhoy Chand
- A Study to Evaluate Symptoms in Essential Hypertension Using Random Forest Decision Tree Algorithm
Abstract Views :493 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, The University of Burdwan, Golapbag, Burdwan-713104, IN
2 Department of Computer & Information Science, Dr. B. C. Roy Engineering College, Dugrapur-713206, IN
3 Department of Computer Science, Bagnan College, W.B., IN
1 Department of Computer Science, The University of Burdwan, Golapbag, Burdwan-713104, IN
2 Department of Computer & Information Science, Dr. B. C. Roy Engineering College, Dugrapur-713206, IN
3 Department of Computer Science, Bagnan College, W.B., IN
Source
Indian Science Cruiser, Vol 31, No 4 (2017), Pagination: 28-35Abstract
In the present study, we would like to gain the insight of the medical data through classification based data mining technique, namely random forests classification. The paper presents a hypertension risk factor symptom classification task where the decisions should be made only on the basis of general information and basis biochemical data. Even though advancements in the field of medicine make it easier to treat hypertension, there are still insufficiencies regarding the determination and evaluation of its risk factors. In this study, various risk factors used to diagnose were investigated by taking into consideration the individuals with common symptoms and complaints. Patient data were collected from a homeopathic medical practitioner. Present analysis predicts that Hypertrophy of Heart and allied, Stiffness of neck and Sensitivity to noise are most important risk symptom to predict hypertension.Keywords
Classification, Essential Hypertension, Random Forests Classifier, Confusion Matrix.
References
- E.B. Blanchard, J.E. Martin, and P.M. Dubbert, (1988). Non-drug treatments for essential hypertension. New York: Pergamon Press.
- G. Stainbrook, (1988). Stress management and hypertension. In M.L. Rusell (Ed.). Stress management for chronic disease (pp. 156-174). New York: Pergamon Press.
- C.B. Schechter, (1990). Sequential decision making with continuous disease states and measurements. II. Applications to diastolic pressure. Medical Decision Making, 10, 256-265.
- M.P. Garcia-Vera, and J. Sanz, (2000). Tratamientos cognitivo-conductuales para la hipertension esencial. In L. Oblitas and Becona (Eds.). Psicologia de la salud. México: Editorial Plaza y Valdés.
- F. Turk, N. Barisci, A. Ciftci, Y. Ekmekci, “Comparison of Multi Layer Perceptron and Jordan Elman Neural Networks for Diagnosis of Hypertension”, Intelligent Automation & Soft Computing, Volume 21, Issue 1, 2015.
- Z. M. Zhu, “The evaluation and control of metabolic risk in essential hypertension,” South China Journal of Cardiovascular Diseases, vol. 14, no. 2, pp. 80–81, 2008.
- M. D. Cheitlin, M. Sokolow, & M. B. Mcllroy, (1993). Systemic hypertension. Clinical cardiology. Englewood Cliffs, NJ: Prentice-Hall.
- I. Jo, Y. Ahn, J. Lee, K. R. Shin, H. K. Lee, & C. Shin, (2001). Prevalence, awareness, treatment, control and risk factors of hypertension in Korea: The Ansan study. Journal of Hypertension, 19(9), 1523–1532.
- N. Nakanishi, W. Li, H. Fukuda, T. Takatorige, K. Suzuki, & K. Tatara, (2003). Multiple risk factor clustering and risk of hypertension in Japanese male office workers. Industrial Health, 41, 327–331.
- R. R. Williams, S. C. Hunt, & S. J. Hasstedt, (1989). Current knowledge regarding the genetics of human hypertension. Journal of Hypertension, 7(Suppl. 6), 8.
- DM Farid, L Zhang, CM Rahman, MA Hossain, R. Strachan Hybrid decision tree and naı¨ve Bayes classifiers for multiclass classification tasks. Expert Syst Appl 2014;41(4):1937–46.
- P. Riccardo and C. Stefano 1991 “A Neural Network Expert Systemfor Diagnosing and Treating Hypertension” University of Florence Riccardo Livi, pp.64-71.
- C. Sylvie, G. Sylvie, M Gilles., and P. S. Jean, 2000 “Statistical and Fuzzy Models of Ambulatory Systolic Blood Pressure for Hypertension Diagnosis” IEEE Transactions on Instrumentation And Measurement, Vol. 49, No. 5, pp.998-1003
- Novruz ALLAHVERDI, Serhat and TORUN Ismail SARITAS, 2007 “Fuzzy Expert System Design for Determination of Coronary Heart Disease Risk” International Conference on Computer Systems and Technologies .
- D. Pandey, Mahajan Vaishali & Srivastava Pankaj 2006 “Rule Based System for Cardiac Analysis”, NATL ACAD SCI LETT, Vol. 29, No. 7&8, pp 299-309
- Srivastava Pankaj, Srivastava Amit 2012 “A Soft Computing Approach for Cardiac Analysis” Journal of Basic and Applied Scientific Research, 2(1)376-385
- “Fuzzy Based High Blood Pressure Diagnosis” by Vishal Chandra, Pinki Singh et. al. International Journal of Advanced Research in Computer Science & Technology (IJARCST), Vol. 2, Issue 2, Ver. 1 (April - June 2014),[ ISSN : 2347 - 9817 ].
- B. Sumathi, and Dr A. Santhakumaran. “Pre-diagnosis of hypertension using artificial neural network.” Global Journal of Computer Science and Technology 2011.
- Samant, Rahul, and Srikantha Rao. “Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension.” International Journal of Computer Applications 2013.
- Srivastava, Pankaj, et al. “A Note on Hypertension Classification Scheme and Soft Computing Decision Making System.” ISRN Biomathematics 2013.
- Das, Sujit, Pijush Kanti Ghosh, and Samarjit Kar. “Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system.” Fuzzy Systems, IEEE International Conference on. IEEE, 2013.
- Y. M. Chae, S. H. Ho, K. W. Cho, D. H. Lee, & S. H. Ji, (2001). Data mining approach to policy analysis in a health insurance domain. International Journal of Medical Informatics, 62, 103–111.
- Dr. Dilip Bhattacharyea, Homeopathy through the Spectacles of Psychiatry, Geeta Prakashani.
- Dr. Dilip Bhattacharyea, Unveiling Some Secrets of Homeopathy, The Unknown in the Known, Geeta Prakashani.
- F Basciftci. A Eldem. “Using reduced rule base with Expert System for the diagnosis of disease in hypertension”, Medical and Biological Engineering & Computing 2013; 51:1287–1293.
- S.Ray, A.C. Mondal, A. Neogi, “Studies on performance evaluation of homeopathic treatment in hypertension using principal component analysis”, IMS Manthan, , vol. X, Issue I, Jan-June, 2015
- DR Cutler, Edwards Jr TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. Random forests for classification in ecology. Ecology 2007;88(11):2783–92.
- B Ghimire, J Rogan, J. Miller Contextual land-cover classification: incorporating spatial dependence in land cover classification models using random forests and the Getis statistic. Remote Sens Lett 2010;1(1):45–54.
- Gislason PO, Benediktsson JA, Sveinsson JR. Random forests for land cover classification. Pattern Recogn Lett 2006;27 (4):294–300.
- L Guo, N Chehata, C Mallet, S. Boukir Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. ISPRS J Photogrammetry Remote Sens 2011;66(1):56–66.
- Chen XW, Liu M. Prediction of protein–protein interactions using random decision forest framework. Bioinformatics 2006;21(24):4394–400.
- O¨ zc- ift A. Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. Comput Biol Med 2011;41(5):265–71.
- M Seera, CP. Lim A hybrid intelligent system for medical data classification. Expert Syst Appl 2014;41(5):2239–49.
- JI Titapiccolo, M Ferrario, S Cerutti, C Barbieri, F Mari, E Gatti, MG. Signorini Artificial intelligence models to stratify cardiovascular risk in incident hemodialysis patients. Expert Syst Appl 2013;40(11):4679–86.
- L. Breiman Random forests. Mach Learning 2001;45(1):5–32.
- http://journal.r-project.org/archive/2009-2/RJournal_2009-2_Williams.pdf
- A Study to Identify the Manner of Death in Head Injury Using Classification Model
Abstract Views :411 |
PDF Views:6
Authors
Affiliations
1 Department of Computer Science, The University of Burdwan, Golapbag, Burdwan- 713104, IN
2 Department of Forensic & State Medicine, Burdwan Medical College, Burdwan- 713104, IN
3 Department of Computer & Information Science, Dr. B. C. Roy Engineering College, Durgapur-713206, IN
1 Department of Computer Science, The University of Burdwan, Golapbag, Burdwan- 713104, IN
2 Department of Forensic & State Medicine, Burdwan Medical College, Burdwan- 713104, IN
3 Department of Computer & Information Science, Dr. B. C. Roy Engineering College, Durgapur-713206, IN
Source
Indian Science Cruiser, Vol 32, No 3 (2018), Pagination: 26-35Abstract
Head injury is the leading cause of death found at medico legal autopsy. This may be due to various mechanisms and thereby the nature of death in fatal head injury assumes utmost importance in forensic practice. It is often expected and hence required that a forensic pathologist apart from ascertaining the cause of death should indicate the nature of death. This has to be done on a sound scientific and objective manner as far as possible. There is need for an autopsy proven method to broadly classify the wounds into homicidal blows, accidental trauma and fall from height. The present investigation is designed to explore a classification model for identifying the nature of death from head injury.Keywords
Principal Component Analysis, Classification, Head Injuries, Burdwan Medical College, Autopsy, Eigenvalues, Forensic, Skull.References
- C. Brunel, C. Fermanian, M. Durigon, and D.L. Grandmaison, “Homicidal and suicidal sharp force fatalities: Autopsy parameters in relation to the manner of death”, Forensic Sci. Int, vol. 198, 2010, pp.150154.
- Buck, T. J., Vidarsdottir, U.S. “A proposed method for the identification of race in sub-adult skeletons: a geometric morphometric analysis of mandibular morphology”, J Forensic Sci, vol.49, no.6, 2004, pp.1159-1164.
- Chandra, J., Dogra, T.D., and Dikshit, P.C. “Patterns of cranio-intracranial injuries in fatal vehicular accidents in Delhi, (1966-1976)”, Medicine Science Law,vol. 19, no. 3,1979, pp.186-194.
- Guler, I., Gokcil, Z., and Gulbandilar, E. “Evaluating of traumatic brain injuries using artificial neural networks”, Expert Systems with Applications, vol.36, 2009, pp.10424–10427.
- Guler, I., Tunca, A.B., and Gulbandilar, E. “Detection of traumatic brain injuries using fuzzy logic”, Expert Systems with Applications, vol.34, 2008, pp.1312– 1317.
- Hotteling, H. “Analysis of a complex of statistical variables into principal components,” Journal of Educational Psychology, vol.24, no. 6, 1933, pp. 417.
- Ji, S., Smith, R., Huynh, T., and Najarian, K. “A comparative analysis of multi-level computer-assisted decision making system for traumatic brain injuries”, BMC Medical Informatics and Decision Making, vol.9, no.2, 2009.
- Kremer, C., Racette, S., Dionne, C. and Sauvageau, A. “Discrimination of Falls and Blows in Blunt Head Trauma: Systematic Study of the Hat Brim Line Rule in Relation to Skull Fractures”, Journal of Forensic Sciences, vol. 53, no.3, 2008, pp.716 -719.
- Lefèvre, T., Alvarez, J.C., Grandmaison, L.D.L.G. “Discriminating factors in fatal blunt trauma from low level falls and homicide”, Forensic Sci Med Pathol, vol.11, no.2, 2015, pp.152–161.
- Mandal, S.N., Choudhury, J.P., Chaudhury, S.R.B. “Neuro-PCA-Factor Analysis in Prediction of Time Series Data”, American Journal of Intelligent Systems, 2012.
- Mondal, A.C., Ray, S., Dey, K. and Neogi, A. “Studies on Performance Evaluation of Homeopathic
- Treatment for Essential Hypertension Using Principle Component Analysis”, The Journal of Management, Information Technology and Journalism, IMS Manthan, Noida(India),Jan-June 2015; Vol. X, Issue 1
- Mukhopadhyay, P.P., Ghosh, S., and Ghosh, T.K. “Forensic diagnosis and classification of snakebite (neurotoxic and haemotoxic) from renal changes at autopsy: morphological and histological determinants using a semi-quantitative method”, Med Sci Law, vol.50, no.3, 2010, pp140-144.
- Murtag, F. and Heck, A. Multivariate Data Analysis, Reidel Publishing Company, Dordrecht, Holland, 1987.
- Patil, A.M., and Vaz, W.F. “Pattern of Fatal Blunt Head Injury: A Two Year Retrospective / Prospective Medico Legal Autopsy Study”, J Indian Acad Forensic Med, vol. 32, no. 2, 2010, pp.144-149.
- Pearson, K. “On lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, 1901, pp. 559–572.
- Punia,R.K., Yadav, A., and Lalchand, “Pattern of Head injuries in Homicidal deaths at Jaipur During 201213, India: An Autopsy based Study”, J Indian Acad Forensic Med, vol. 36, no. 3, 2014, pp.242-245.
- Ray, S., Mondal, A.C., and Neogi, A. “A study of the development of principal component analysis using neural network in assessment of higher education system”, in the proceedings of National Conference of NaCCS-2012, held at The University of Burdwan (Dept. of Computer Science), Burdwan, India, 15th March-16th March,2012, pp.100-104.
- Saatman, K.E., Duhaime, A., Bullock, R., Maas, A. I. R., Valadka, A., Manley, G. T. and Workshop Scientific Team and Advisory Panel Members, “Classification of traumatic brain injury for targeted therapies”, Journal of Neurotrauma, vol. 25, no.7, 2008, pp.719-738.
- Sumei, Z. “The Comprehensive Evaluation of Teaching Quality Based on Principal Component Analysis”, in Department of applied Mathemtatics, School of Science, Xi’an, China, 2010.
- Yin, H., Li, G., Leong, T.Y., Kuralmani, V., Pang, H., Ang, B.T., Lee, K.K., and Ng, I. “Experimental Analysis on Severe Head Injury Outcome Prediction—A Preliminary Study”, Technical Report TRD9/06, School of Computing, National University of Singapore, 2006.
- Zhang, T., Guo, J. and Zhang, N. “Application of PCA in Quality Assessment of College Students”, IEEE International Conference on Information Technology, Computer Engineering and Management Sciences, 2011.
- http://www.cyclismo.org/tutorial/R/
- http://www.mathworks.com