Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    Deepika Yadav,

  • Aarti Sehwag,

  • Heena Kwatra,

  • Nitasha Rathore,

  • Amrita Ticku,

  • Jyotsna,

  1. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  2. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  3. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  4. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  5. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  6. Assistant Professor, Department of Computer Science and Engineering, Guru Tegh Bahadur Institute of Technology, New Delhi, India

Abstract

Early and reliable assessment of liver health is essential for timely treatment, yet most machine-learning approaches face limitations such as class imbalance and low clinical interpretability. This study proposes a polymer-integrated, explainable machine-learning framework that combines SMOTE-based data balancing, Logistic Regression, and XAI techniques (SHAP and LIME) for transparent liver-health classification. In addition to ML modelling, the study emphasizes the emerging role of polymer-based biosensors, microfluidic polymer chips, polymer nanomaterials, and polymer–nanoparticle diagnostic technologies for liver-function assessment. These polymer platforms generate high-sensitivity biochemical signals for biomarkers such as bilirubin, ALT, and AST, making them ideal companions to AI-based decision systems. Using the (ILPD) Indian Liver Patient Dataset, the proposed framework achieved an overall accuracy of 78%, with balanced F1-scores for both healthy (0.79) and at-risk individuals (0.76). SHAP and LIME consistently identified s1, sex, s5, and blood pressure as the most influential predictors, aligning with clinical literature. This liver disease is deadly disease that need proper treatment and care the foremost requirement is early diagnosis so that the disease can be cured on initial stages. Approximately 1.4–1.43 million deaths per year are caused due to cirrhosis and other chronic liver diseases on a global scale. The interdisciplinary integration of Machine Learning, XAI, and polymer-based diagnostic technologies highlights a promising path for advanced, sensitive, and explainable polymer-AI health-monitoring systems. By bridging polymer-based diagnostic innovations with explainable machine learning, this review aims to support the development of transparent, accurate, and clinically acceptable tools for liver health assessment and personalized healthcare.

Keywords: Polymer biosensors, Polymer nanomaterials, Liver disease prediction, Explainable AI, Machine learning, SMOTE, Logistic Regression, SHAP, LIME.

How to cite this article:
Deepika Yadav, Aarti Sehwag, Heena Kwatra, Nitasha Rathore, Amrita Ticku, Jyotsna. Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Deepika Yadav, Aarti Sehwag, Heena Kwatra, Nitasha Rathore, Amrita Ticku, Jyotsna. Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243558


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Ahead of Print Subscription Original Research
Volume 14
03
Received 09/02/2026
Accepted 10/03/2026
Published 12/05/2026
Publication Time 92 Days
2

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