Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

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

    G. Punithakumari,

  • B. Suresh,

  1. Associate Professor, Department of Chemistry, J.J. College of Engineering and Technology, Tiruchirapalli, Tamil Nadu, India
  2. Assistant Professor, Department of Physics, V.S.B. Engineering College, (An Autonomous Institution), Karur, Tamil Nadu, India

Abstract

A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). The models were assessed using multiple evaluation metrics such as performance score, reliability, prediction accuracy, and error rate. Experimental results demonstrate that complex machine learning models generally achieve higher prediction accuracy compared to simpler traditional approaches. Classical algorithms such as Decision Tree and Logistic Regression provide stable baseline results but tend to produce higher prediction errors in complex datasets. Support Vector Machines and Random Forest models improve generalization performance through structured optimization and variance reduction techniques. Artificial Neural Networks further enhance dataset accuracy by effectively capturing nonlinear relationships and interactions among features. The findings indicate that models with a well-balanced bias–variance tradeoff achieve the highest reliability, lowest error rate, and best overall prediction accuracy. Feature importance analysis also reveals that a limited number of critical input variables strongly influence system performance. Furthermore, hybrid and ensemble learning strategies consistently outperform individual standalone models in complex prediction tasks.

Keywords: Biomedical Polymer Nanocomposites, Biocompatibility Prediction, Random Forest, Artificial Neural Networks, Support Vector Machine.

How to cite this article:
G. Punithakumari, B. Suresh. Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
G. Punithakumari, B. Suresh. Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243773


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Ahead of Print Subscription Original Research
Volume 14
03
Received 09/03/2026
Accepted 20/03/2026
Published 13/05/2026
Publication Time 65 Days
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