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Venkatesan, P.

• Correlated Frailty Model: an Advantageous Approach for Covariate Analysis of Tuberculosis Data

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Authors

C. Ponnuraja ¹, P. Venkatesan ¹

Affiliations
1 Tuberculosis Research Centre (ICMR), Chennai-600 031, IN

Source

Indian Journal of Science and Technology, Vol 3, No 2 (2010), Pagination: 151-155

Abstract

The demonstration of varying treatment effects among different subjects of patients is an important part of the analysis of clinical trials. But issues of censoring, truncation and inclusion criteria complicate the analysis of clinical trial data. Recent advances in proportional hazard methodologies provide regression diagnostics, improved point and interval estimates of the parameters of survival functions, handling of time dependent covariates in the analysis. This paper discusses the interactions between treatment and patient in the presence of censoring and to account heterogeneity using frailty model. The application of the frailty model with respect to pulmonary tuberculosis data are presented and discussed.

Keywords

Tuberculosis, Cox Proportional Hazard Model, Time-dependent Covariates, Gamma Shared Frailty Model

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References

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• Tuberculosis Research Centre, ICMR, Chennai, India (2004) Split-drug regimens for the treatment of patients with sputum smear-positive pulmonary tuberculosis- a unique approach. Trop. Med. Int. Health. 9, 551-58.
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• Vu H, Segal MR, Knuiman MW and James IR (2001) Asymptotic and small sample statistical properties of random frailty variance estimates for shared gamma frailty models. Comm. Stat—Simulation Comp. 30, 581–595

• Modeling the Spatial Variogram of Tuberculosis for Chennai Ward in India

Abstract Views :475  |  PDF Views:124

Authors

P. Venkatesan ¹, R. Srinivasan ¹

Affiliations
1 Tuberculosis Research Centre, ICMR, Chennai – 600 031, IN

Source

Indian Journal of Science and Technology, Vol 3, No 2 (2010), Pagination: 167-169

Abstract

In this paper, we have used statistical measures and spatial deviational ellipse to determine the spatial pattern of tuberculosis within a Chennai ward population to gain insight into the disease spread. Variogram is used to describe the spatial dependence of tuberculosis in Chennai wards and it is compared with theoretical variogram model of spherical, Gaussian and exponential fitted to tuberculosis data. Arc View GIS 9.2 and SAS software were used for spatial analysis of tuberculosis spread. Data were obtained from District Hospital records for Chennai wards. The results of the spatial pattern revealed that the spread of tuberculosis in Chennai wards have been diverse, with many wards having a low rate of infection and the epidemic being most extreme in slum areas. Variogram increases with distance at small distances and then level off which implies spatial dependence exists between small distance of tuberculosis cases. Spherical model fits data better. Spatial analysis is proved to be more useful for studying spread of tuberculosis analysis and modeling of disease analysis.

Keywords

Bayesian, Disease Mapping, Variogram, Spatial Correlation And Deviational Ellipse

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References

• Cressie N and Hawkins DM (1980) Robust estimation of the variogram. J. Internet. Assoc. Math. Geol. 12, 115-125
• Garcıa Soidan P (2003) Local linear regression estimation of the variogram. Statist. Probab. Lett. 64, 169-179.
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• Genton M (1998) Highly robust variogram estimation. Math. Geol. 30, 213-221.
• Isaaks EM and Srivastava RM (1989), In Introduction to Applied Geostatistics, Oxford University, NY.
• Maglione DS and Diblasi AM (2004) Exploring a valid model for the variogram of an intrinsic spatial process. Stoch. Envir. Res. Risk Ass. 18, 366-376.
• Menezes R, Garcia Soidán P and Febrero Bande M (2005), A comparison of approaches for valid variogram achievement. J. Comput. Stat. 20, 623 -640.
• Venkatesan P and Srinivasan R(2008), Applied Bayesian statistical Analysis. Proceeding of NSABSA 2008, 51-56

• Survival Models for Exploring Tuberculosis Clinical Trial Data - An Empirical Comparison

Abstract Views :442  |  PDF Views:112

Authors

C. Ponnuraja ¹, P. Venkatesan ¹

Affiliations
1 Tuberculosis Research Centre (ICMR), Chennai-600 031, IN

Source

Indian Journal of Science and Technology, Vol 3, No 7 (2010), Pagination: 755-758

Abstract

The proportional hazard (PH) model and its extension are used comprehensively to assess the effect of an intervention in the presence of covariates. The assumptions of PH model may not hold where the effect of the intervention is to accelerate the onset of an event. The accelerated failure time (AFT) model is the alternative when the PH assumption does not hold. The aim of this paper is to formulate a model that yields biological plausible and interpretable estimates of the effect of important covariates on survival time. The data consists of 1236 tuberculosis patients admitted in randomized controlled clinical trial. A total of six covariates are considered for modeling. The AFT model gives better prediction than the Cox PH model.

Keywords

Accelerated Failure Time Model, Proportional Hazards Model, Time Dependent Covariate, Tuberculosis

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References

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• Fleming TR and Lin DY (2000) Survival analysis in clinical trials: Past developments and future directions. Biometrics. 56, 971-983.
• Hernan MA, Cole SR, Margolick J, Cohen M and Robins JM (2005) Structural accelerated failure time models for survival analysis in studies with timevarying treatments. Pharmacoepidemiol. Drug safety. 14, 477–491.
• Huang J, Ma S and Xie H (2007) Least absolute deviations estimation for the accelerated failure time model. Statistica Sinica. 17, 1533-1548.
• Jin Z, Lin DY and Ying Z (2006) On least-squares regression with censored data. Biometrika. 93,147- 161.
• Jin Z, Lin DY, Wei LJ and Ying ZL (2003) Rank-based inference for the accelerated failure time model. Biometrika. 90, 341-353.
• Kalbfleisch JD and Prentice RL (1980) The statistical analysis of failure time data, John Wiley & Sons, NY.
• Kay R and Kinnersley N (2002) On the use of the accelerated failure time model as an alternative to the proportional hazards model in the treatment of time to event data: A case study in influenza. Drug Inform. J. 36, 571–579.
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• TRC (Tuberculosis Research Centre, ICMR, Chennai, India) (2004) Split-drug regimens for the treatment of patients with sputum smear-positive pulmonary tuberculosis-a unique approach. Tropical Med. Int. Health. 9, 551-558.

• A Comparative Study of Principal Component Regression and Partial least Squares Regression with Application to FTIR Diabetes Data

Abstract Views :851  |  PDF Views:165

Authors

P. Venkatesan ¹, C. Dharuman ², S. Gunasekaran ³

Affiliations
1 Department of Statistics, Tuberculosis Research Centre, ICMR, Chennai-600 031, IN
2 P. G. Department of Mathematics, Pachaiyappa’s College, Chennai-600 030, IN
3 P. G. Department of Physics, Pachaiyappa’s College, Chennai-600 030

Source

Indian Journal of Science and Technology, Vol 4, No 7 (2011), Pagination: 740-746

Abstract

In recent years, Fourier Transform Infrared (FT-IR) spectroscopy has had an increasingly important role in the field of pathology and diagnosis of disease states. The principal component regression (PCR) and the partial least squares regression (PLS) are the often proposed methods and widely used in FTIR data analysis, when the number of explanatory variable is relatively large in comparison to the samples as the least squares estimator may fail in such situations. They provide biased estimators with the relatively smaller variation than the variance of the least squares estimators. In this paper, a FTIR diabetes dataset is used in order to examine the performance of the two biased regression models on prediction. The conclusion is that for prediction PCR and PLS provides similar results which require substantial verification for any claims as to the superiority of any of the two biased regression methods.

Keywords

Fourier Transform Infrared, Principal Component Regression, Partial least Square, Diabetes Data

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References

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• HIV/AIDS Projection in Tamilnadu Using Back Calculation Method

Abstract Views :451  |  PDF Views:124

Authors

P. Venkatesan ¹, D. Ramamurthy ², N. Sundaram ³

Affiliations
1 Department of Statistics, National Institute for Research in Tuberculosis, ICMR, Chennai – 600031, IN
2 Department of Mathematics, Sir Theagaraya College, Chennai – 600 021, IN
3 Department of Statistics, Dr. Ambedkar Government Arts College, Chennai - 600 039, IN

Source

Indian Journal of Science and Technology, Vol 5, No 8 (2012), Pagination: 3157-3162

Abstract

The current prevalence of HIV infection and the corresponding pattern of incidence from the beginning of the epidemic to the present time are mainly estimated by means of back-calculation method. This back-calculation method reconstructs the past pattern of HIV infection and predicts the future number of AIDS cases with the present infection status. The basic data required for back-calculation methodology is the number of AIDS cases over a period of time. TANSACS publishes the reported number of AIDS cases in Tamil Nadu. In this paper, the various approaches for modeling the incubation distribution are compared using real data under various infection density distributions. The projected minimum and maximum AIDS cases in Tamil Nadu, a southern state of India, based on the reported data are 3702712 and 6936047 respectively. These estimates are based on the unadjusted AIDS incidence data. The purpose of this paper is to review the contribution of back-calculation method to our understanding of the AIDS and to summarize and interpret the epidemiological findings.

Keywords

HIV/AIDS,Incubation Period, Estimation, Infection Distributions, Back Calculation

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References

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• Treatment Response Classification in Randomized Clinical Trials: A Decision Tree Approach

Abstract Views :457  |  PDF Views:152

Authors

P. Venkatesan ¹, N. R. Yamuna ¹

Affiliations
1 Department of Statistics, National Institute for Research in Tuberculosis, (ICMR), Chennai-600 031, IN

Source

Indian Journal of Science and Technology, Vol 6, No 1 (2013), Pagination: 3912-3917

Abstract

Decision Trees are a subfield of machine learning technique within the larger field of artificial intelligence. It is a supervised learning technique for classification and prediction. The decision trees are widely used for outcome prediction under various treatments for disease cure, prevention, toxicity and relapse. The aim of the paper is to compare the decision tree algorithms in classifying tuberculosis patient's response under randomized clinical trial condition. Classification of patient's responses to treatment is based on bacteriological and radiological methods. Three decision tree approaches namely C4.5, Classification and regression trees (CART), and Iterative dichotomizer 3 (ID3) methods were used for the classification of response. The result shows that C4.5 decision tree algorithm performs better than CART and ID3 methods.

Keywords

Decision Tree, Data Mining, Cart. C4.5, ID3

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• Exponentiated Exponential Models for Survival Data

Abstract Views :603  |  PDF Views:74

Authors

P. Venkatesan ¹, N. Sundaram ²

Affiliations
1 Department of Statistics, Tuberculosis Research Centre, ICMR, Chennai-600 031, Tamilnadu, IN
2 Department of Statistics, Dr.Ambedkar Government Arts College (Autonomous), Vyasarpadi, Chennai-600 039, Tamilnadu, IN

Source

Indian Journal of Science and Technology, Vol 4, No 8 (2011), Pagination: 923-930

Abstract

The Exponentiated Exponential (EE) model serves as an alternative to Exponential, Weibull and Gamma models. It is observed that EE model has been used in the analysis of complete life time data. In this paper an attempt has been made to study the modeling of censored survival data and the results are compared with other models. Log Likelihood ratio statistic and Cox-Snell residuals are used for the comparisons. The EE model performs better than Exponential and Weibull models. We also fitted Log-logistic model and compared with other models based on Baysian information criterion (BIC) and an information criterion (AIC). The Log-logistic model also performs better than the above models in situations when the censoring is at low level.

Keywords

EE Model, Hazard Function, Life Time Data, Survival Function, Weibull Model

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• Clustering of Disease Data Base using Self Organizing Maps and Logical Inferences

Abstract Views :610  |  PDF Views:340

Authors

P. Venkatesan ¹, M. Mullai ²

Affiliations
1 Department of Statistics, NIRT(ICMR), Chennai, IN
2 Department of Mathematics, Ethiraj College For Women, Chennai, IN

Source

Indian Journal of Automation and Artificial Intelligence, Vol 1, No 1 (2013), Pagination: 2-6

Abstract

Disease classification requires an expertise in handling the uncertainty. ANNs emerge as a powerful tool in this regard. ANNs have featured in a wide range of applications with promising results in biomedical sciences. The self-organized maps (SOM) use unsupervised learning to produce low dimensional discretized representation of the input space. SOMs are different from other neural networks in the sense that they use neighborhood function to preserve the topological properties of the input space. This paper compares Kohanen's SOM network with other clustering method. The SOM gives faster and accurate results in clustering the data. The results were presented and compared.

Keywords

Medical Diagnosis, Artificial Intelligence (AI), Neural Network, Self Organizing Map(SOM), Best Matching Unit(BMU),Tuberculosis (TB)

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• Rough Set Theory Approach for Attribute Reduction

Abstract Views :589  |  PDF Views:379

Authors

R. A. Lukshmi ¹, P. V. Geetha ¹, P. Venkatesan ²

Affiliations
1 Department of Mathematics, Meenakshi College for Women, Chennai-24, IN
2 Department of Statistics, National Institute for Research in Tuberculosis, ICMR, Chennai-31, IN

Source

Indian Journal of Automation and Artificial Intelligence, Vol 1, No 3 (2013), Pagination: 70-80

Abstract

Knowledge Discovery from databases is practically important in many fields , including the field of medicine. Many methods are being developed for knowledge discovery and due to the availability of enormous amount of data, extraction of knowledge from database has become a challenging task. Researchers have proved methods, among which Rough Set Theory is an effective tool for knowledge discovery. In this paper, Rough Set Theory and its basic ideas are reviewed and applied to identify symptoms for diagnosing diabetes. This study also presents methods for extension to high dimensional data.in the medical domain.

Keywords

Knowledge Discovery, Rough Set Theory, Discernibility Matrix, Reduct, Rule Extraction

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References

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• Sakr, A., & Mosa, D. I. A. N. A. (2010) Dealing medical data with fundamentals of new artificial intelligence. International Journal of Engineering Science and Technology, 2(9), 4406-4417.
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• Sreevani, Y. V., & Rao, T. V. N. (2010). Identification and Evaluation of Functional Dependency Analysis using Rough sets for Knowledge Discovery. International Journal. of Advanced Computer Science and Applications.
• Suraj, Z. (2004) An Introduction to Rough Set Theory and Its Applications. ICENCO, Cairo, Egypt.
• Xu, Y., Cao, Y., & Yang, S. (2011). Research on Care of Postoperative Patient based on Rough Sets Theory. International Journal of Computer Applications,31(10).
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• Socio-economic Correlation with Extent of Adoption of Indigenous Tribal Agricultural Practices

Abstract Views :186  |  PDF Views:0

Authors

P. Venkatesan ¹, M. Sundaramari

Affiliations
1 Depar tment of Agricultural Extension, Krishi Vigyan Kendra, Gandhigram Rural Institute, Gandhigram, Dindigul (T.N.), IN

Source

Agriculture Update, Vol 8, No 1 & 2 (2013), Pagination: 110-114

Abstract

No abstract

Keywords

Socio Economic Characteristics, Extent Of Adoption, Indigenous Tribal Agricultural Practices

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• Tuberculosis Disease Classification Using Genetic-neuro Expert System

Abstract Views :310  |  PDF Views:0

Authors

P. V. Geetha ¹, R. A. Lukshmi ¹, P. Venkatesan ²

Affiliations
1 Department of Mathematics, Meenakshi College for Women, Chennai–600 024, IN
2 National Institutes for Research in Tuberculosis, ICMR, Chennai-600 031, IN

Source

Indian Journal of Science and Technology, Vol 7, No 4 (2014), Pagination: 421-425

Abstract

This study investigates the application of the hybrid technique Genetic-Neuro approach for Tuberculosis disease classification. Evolutionary algorithms are proved to be the efficient methods for optimization problems and their primary component namely Genetic Algorithm is used to select the significant features for Disease Classification. Artificial Neural Network is used for classification and the training is done by methods like Levenberg Marquardt algorithm. The construction process of the system is illustrated by using tuberculosis disease data. The results reveal that the hybrid technique Genetic-Neural system outperforms the conventional technique Artificial Neural Network for disease classification.

Keywords

Feature Selection, Genetic Algorithm, Neural Network, Tuberculosis

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• Functional Response of Tenagogonus fluviorum (Fabricius) in its Predation of Culex quinquefasciatus Say Larvae of Varied Density and Size

Abstract Views :222  |  PDF Views:101

Authors

S. Arivoli ¹, P. Venkatesan ¹

Affiliations
1 Aquatic Entomology and Biocontrol Research Laboratory Post Graduate and Research Department of Advanced Zoology & Biotechnology, Loyola College, Chennai, 600 034, IN

Source

Journal of Biological Control, Vol 20, No 1 (2006), Pagination: 19-24

Abstract

Male and female Tenagogottus fluviorum (Fabricius) were exposed to different size classes (I, II, III, IV and pupa) and different densities (25, 50, 75, 100, 125) of Culex quinquefasciatus larvae as prey. Predatory efficiency of the female bug is higher than that of male. However, both the sexes of the predator prefer large sized prey. The size in predatory performance of the bug is directly proportional to the increase in prey density. Attack rate and handling time as two statistical constants were derived from the prey death rate caused by the predator and their significance is discussed.

Keywords

Culex quinquefasciatus, Functional Response, Predation, Tenagogonus fluviarum.

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• TB Disease Diagnosis Using Fuzzy Max-Min Composition Technique

Abstract Views :168  |  PDF Views:2

Authors

M. Muthuvijayalakshmi ¹, E. Kumar ², P. Venkatesan ¹

Affiliations
1 Department of Statistics, National Institute for Research in Tuberculosis, (ICMR), Chennai-31, IN
2 Department of Statistics, Presidency College, Chennai-5, IN

Source

Fuzzy Systems, Vol 6, No 1 (2014), Pagination: 

Abstract

The paper attempts to model a classification problem to examine the Sanchez’s approach for medical diagnosis by the use of a technique called Fuzzy Max-Min composition. Fuzzy logic is one of the best choice to model the relationship between a set of symptoms of a patient and a possible diagnosis. The basic premises of fuzzy logic system are presented and a detailed analysis of fuzzy logic system developed to solve a disease diagnosis problem. The utility of the model is illustrated using tuberculosis patients data from a controlled clinical trial.

Keywords

Classification, Max-Min Composition, Medical Diagnosis, Tuberculosis.

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• Markov Chain Monte Carlo based Pattern Analysis for Diabetic Spectral Data

Abstract Views :243  |  PDF Views:0

Authors

C. Dharuman ¹, P. Venkatesan ²

Affiliations
1 SRM University, Ramapuram Campus, Chennai - 600089, Tamil Nadu, IN
2 Sri Ramachandra University, Porur, Chennai - 600116, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 33 (2015), Pagination: 

Abstract

Markov Chain Monte Carlo (MCMC) methods have been successfully used to overcome problems involving high dimensional diabetic spectral data. The objective is to show that the onset of a disease changes the relative content of the Bio-Molecules. The problem is formulated and solved in Bayesian frame work by using MCMC algorithm. The efficiency of the models are discussed, evaluated and compared.

Keywords

Bayesian, Diabetic Spectral Data, Markov Chain Monte Carlo, Pattern Analysis

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