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All Journal The Eastasouth Journal of Information System and Computer Science
Tushar Roy
St. Francis College, Brooklyn, New York, USA
Computer Science & IT

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Data-Driven Predictive Modeling for Early Diagnosis and Risk Stratification of Chronic Diseases Md Jubayar Hossain; Mohammed Majid Bakhsh; Muhammad Adnan; Evha Rozario; Sraboni Clara Mohonta; Tushar Roy; Md Imtiaz Faruk; Emran Hossain
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1220

Abstract

Chronic kidney disease (CKD) often remains undetected until advanced stages, limiting opportunities for timely intervention and increasing the burden of complications and treatment. This study proposes a data-driven hybrid ResNet50-SVM framework for the early diagnosis and risk stratification of CKD. Clinical data were prepared through missing-value treatment, data cleaning, Min-Max normalization, reduction of redundant information, and optimized feature selection. The processed dataset was divided into training and testing subsets using an 80:20 ratio. ResNet50 was employed to extract high-level representations, while a support vector machine performed the final classification. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion-matrix analysis, and training and validation behavior. The proposed framework achieved an accuracy of 98.76%, precision of 97.65%, recall of 99.21%, and F1-score of 98.34%. It also outperformed logistic regression, a convolutional neural network, and a decision tree across the reported evaluation metrics. The high recall indicates a strong capacity to identify patients with disease while reducing missed positive cases. These findings demonstrate the potential of hybrid deep-learning and machine-learning methods to support accurate, scalable, and timely CKD screening. Further validation using larger, more diverse clinical datasets and explainable AI methods is needed before deployment in real-world clinical decision-support systems.
Business Analytics for United States Retail Channel Planning Under Structural Change Md Jubayar Hossain; Semanto Saha Utsab; Sadia Afrin Jarir; Evha Rozario; Zerin Akter Tanni; Tushar Roy
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1263

Abstract

Retail channel planning requires forecasts that remain useful when purchasing patterns change abruptly. This study examines the quarterly e-commerce share of United States retail sales using 99 observations from 2000 Q1 through 2024 Q3 in the data vintage available on December 31, 2024. Census Bureau estimates distributed through ALFRED provide the sole empirical data source. Six transparent specifications are compared at 59 successive one-quarter forecast origins using chronological estimation. Evaluation distinguishes 2010–2019, 2020–2021, and 2022–2024 Q3 and combines absolute error, squared error, directional bias, and asymmetric planning-loss sensitivity. The lowest mean absolute errors in these periods are 0.0755, 0.9375, and 0.1215 percentage points, respectively. Method rankings depend on the evaluation period and criterion. The findings support period-specific assessment, simple benchmarks, and explicit attention to planning consequences. Results use a fixed historical data vintage; they do not establish causal pandemic effects or realized firm-level savings.
Co-Authors Emran Hossain Evha Rozario Evha Rozario Md Imtiaz Faruk Md Jubayar Hossain Mohammed Majid Bakhsh Muhammad Adnan Sadia Afrin Jarir Semanto Saha Utsab Sraboni Clara Mohonta Zerin Akter Tanni