Browsing by Author "Durgesh Shukla"

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Classification Accuracy of Linear Discriminant Function using Principal Components with Multiple Correlated Variables: A Simulation based Exploration
(Wolters Kluwer Medknow Publications, 2025) Akash Mishra; Sachit Ganapathy; Narayanapillai Sreekumaran Nair; Durgesh Shukla; Ashish Kumar Yadav; Rajaat Vohra; Kuldeep Soni
Background: Linear Discriminant Analysis (LDA) is a powerful and widely used technique for classification with correlated variables. Principal Components (PCs) group these variables into linear combinations and produce independent variables. The LDA on these PC’s may provide better classification accuracy in clinical diagnostics than on usual measurements. Methodology: Two datasets were utilized for demonstration: one from a Sudden Sensorineural Hearing Loss (SSNHL) case-control study and the other from a Gall Bladder (GB) case-control study. Linear Discriminant Analysis (LDA) was conducted on the actual correlated measured variables for group classification, as well as on the derived principal component variables, to compare their classification accuracies. Performance metrics including Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Classification Accuracy, and F1 Score were assessed. For validation, a third simulated dataset was employed. Additionally, LDA was performed on each dataset using eigenvectors of the control group applied to the cases and vice versa, revealing a strong agreement in classification as measured by the kappa statistic. Results: When LDA was applied to the actual lipid measurements in the SSNHL dataset, the classification accuracy was 57.2%, and the F1 score was 39.7%. However, when LDA was performed using principal components (PCs), the classification accuracy markedly improved to 99.2%, with an F1 score of 98.5%. Similarly, for the GB cancer dataset, the classification accuracy and F1 score were initially 77.2% and 77.3%, respectively. Upon applying LDA with the PCs, these metrics were significantly enhanced to 98.4% and 98.3%, respectively. For the simulated dataset, both the classification accuracy and F1 score were 99.1%. The study also demonstrated that the classification accuracy and F1 score remained consistent regardless of whether the eigenvectors from the cases or controls were used to classify new subjects (Kappa Statistic = 0.962, P < 0.001). Conclusion: In group separation, utilizing principal components significantly improves classification accuracy and overall performance metrics, outperforming the use of the original correlated predictors. © 2025 Medical Journal of Dr. D.Y. Patil Vidyapeeth.
Prevalence of disability in Uttar Pradesh: A district-wise study
(Indian Journal of Public Health Research and Development, 2019) Ravi Prakash Jha; Krittika Bhattacharyya; Durgesh Shukla; Nisha Tiwari; Pawan Kumar Dubey
Information on disability is essential for the government to formulate policies, allocate adequate resources and implement appropriate programmes. The objective of this study was to measure the prevalence of disability and the estimation of distribution of disabilities by gender, advancing age, districts, geographical regions,work and marital status in the Uttar Pradesh. Method: We analyzed the 2011 Census data of Uttar Pradesh. Age-adjusted disability rates and disability rates per 100 000 population were calculated. Results: There were 4157514 individuals with disability in Uttar Pradesh in 2011 accounting for a disability rate of 2081 per 100 000 populations. The disability in hearing, seeing, and movement was most predominant with rates of 514, 382 and 339 per 100 000 respectively of the 71 districts, age-standardized disability rates in 20 districts were above the state average of 2081 per 100 000 population. In all kinds of disability there was mostly male predominance in both rural and urban areas across all agecategories. Conclusion: About 2 in every 100 person in Uttar Pradesh (2080 per 100 000 persons) is either physically or mentally disabled. Disability rates reflect the overall health status of the population. Identification of the underlying causes and employing effective and focused preventive strategies will help to reduce the burden of disability and maximize the quality of life in Uttar Pradesh. © 2019, Indian Journal of Public Health Research and Development. All rights reserved.