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Clinical medicine Open Access DOI: 10.1511/identifier

Artificial Intelligence for Early Disease Detection Using Machine Learning Techniques

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Published 14 Mar 2026
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AIIMS Delhi

Abstract

Artificial Intelligence (AI) is transforming modern healthcare by enabling faster and more accurate disease diagnosis. This study explores the use of machine learning algorithms for early disease detection using clinical datasets. The research focuses on building predictive models that analyze patient health records, laboratory results, and demographic information to identify patterns associated with early-stage diseases. Several machine learning techniques including logistic regression, decision trees, and neural networks were implemented and evaluated using publicly available healthcare datasets.

The proposed framework includes data preprocessing, feature selection, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that machine learning models can significantly improve the early detection of diseases compared to traditional diagnostic approaches. The neural network model achieved the highest prediction accuracy among the tested algorithms.

The findings suggest that integrating AI-driven diagnostic tools into healthcare systems can support clinicians in decision-making and improve patient outcomes. However, challenges such as data privacy, dataset bias, and model interpretability remain critical concerns. Future research should focus on developing more transparent and reliable AI models that can be effectively integrated into real-world healthcare environments.

Keywords

artificial intelligence machine learning healthcare analytics disease prediction neural networks medical data analysis

1 Introduction

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2 Background

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3 Research Problem

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4 Research Objectives

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5 Methodology

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6 Results

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7 Discussion

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8 Conclusion

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9 Future Work

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10 References

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