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Bias Detection in Clinically Machine Learning Models
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
The integration of machine learning (ML) into healthcare has enabled significant advances in diagnosis, prognosis, and treatment planning. However, predictive models often inherit biases from imbalanced datasets and structural inequities, leading to unfair outcomes across demographic groups. This project presents a Healthcare Bias Detection Tool, developed in Python with a Tkinter-based interface, to evaluate both the performance and fairness of clinical ML models. The tool supports algorithms such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), while enabling users to load datasets, preprocess data, and configure sensitive attributes and target variables. Beyond traditional metrics like accuracy, precision, recall, and F1-score, the system incorporates fairness indicators including disparate impact ratio and statistical parity difference. Visualizations such as confusion matrices, subgroup accuracy comparisons, and prediction distributions enhance interpretability. The tool also generates structured reports covering performance, bias findings, and recommended mitigation strategies, thus fostering accountability and ethical AI adoption in healthcare. By combining model validation with fairness evaluation, the framework contributes to responsible deployment of machine learning in clinical decision-making.
Keywords
Machine learning, bias detection, fairness metrics, healthcare AI, clinical decision support, support vector machine (SVM), K-nearest neighbors (KNN), disparate impact, statistical parity difference, ethical AI, model evaluation, transparency in AI.
How to cite this paper
@article{1710955,
author = {Manju V A, Harish T A},
title = {Bias Detection in Clinically Machine Learning Models},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {1692-1697},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710955.pdf},
abstract = {The integration of machine learning (ML) into healthcare has enabled significant advances in diagnosis, prognosis, and treatment planning. However, predictive models often inherit biases from imbalanced datasets and structural inequities, leading to unfair outcomes across demographic groups. This project presents a Healthcare Bias Detection Tool, developed in Python with a Tkinter-based interface, to evaluate both the performance and fairness of clinical ML models. The tool supports algorithms such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), while enabling users to load datasets, preprocess data, and configure sensitive attributes and target variables. Beyond traditional metrics like accuracy, precision, recall, and F1-score, the system incorporates fairness indicators including disparate impact ratio and statistical parity difference. Visualizations such as confusion matrices, subgroup accuracy comparisons, and prediction distributions enhance interpretability. The tool also generates structured reports covering performance, bias findings, and recommended mitigation strategies, thus fostering accountability and ethical AI adoption in healthcare. By combining model validation with fairness evaluation, the framework contributes to responsible deployment of machine learning in clinical decision-making.},
keywords = {Machine learning, bias detection, fairness metrics, healthcare AI, clinical decision support, support vector machine (SVM), K-nearest neighbors (KNN), disparate impact, statistical parity difference, ethical AI, model evaluation, transparency in AI.},
month = {September},
}