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A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

Rabia Khushal1, Ubaida Fatima1

  • 1Department of Mathematics, NED University of Engineering & Technology, Pakistan.

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This study introduces fuzzy three-valued logic to better assess heart disease risk by analyzing data uncertainties. The new model significantly improves prediction accuracy and offers personalized health insights.

Keywords:
Artificial Intelligence (AI)Fuzzy three-valued logicHealthcare datasetHeart diseaseMachine learningMedicine dataset

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Fuzzy Logic Systems

Background:

  • Traditional machine learning models struggle with data uncertainties in medical datasets.
  • Accurate prediction of heart disease risk is crucial for timely intervention and patient management.

Purpose of the Study:

  • To introduce a novel fuzzy three-valued logic architecture for handling uncertainties in medical datasets.
  • To enhance the prediction accuracy of heart disease risk assessment.
  • To develop a hybrid fuzzy-modified machine learning model for improved decision-making.

Main Methods:

  • Applied fuzzy three-valued logic to a heart disease dataset, modifying binary inputs to three values (0, 0.5, 1).
  • Integrated fuzzy logic with traditional machine learning techniques to create a hybrid model.
  • Utilized Wilcoxon signed rank test for statistical analysis and cross-domain validation.

Main Results:

  • Achieved a significant increase in machine learning accuracy from 70% to 99%.
  • Reduced computation time to under 11 seconds, demonstrating computational efficiency.
  • The inclusion of a 'may be present' risk category (0.5) provides actionable health insights.

Conclusions:

  • The proposed fuzzy three-valued logic model effectively handles data uncertainties for improved heart disease risk prediction.
  • The hybrid model offers a computationally efficient and highly accurate approach to medical data analysis.
  • Enhanced decision-making support for individuals regarding lifestyle choices to mitigate heart disease risk.