Earticle

초록

영어

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms—Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification—but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

목차

Abstract
1. Introduction
2. Literature Review
3. Proposed model
3.1 Logistic regression
3.2 Random Forest
3.3 Proposed model
3.4 Data
3.5 Cross validation
4. Experiment
4.1 Evaluation metrics
4.2 Experimental result
5. Conclusion
References

키워드

저자

• ByungJoo Kim [ Professor, Department of Electrical and Electronics Engineering Youngsan University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380