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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Fuzzy quantum machine learning (FQML) logic for optimized disease prediction.

Rabia Khushal1, Dr Ubaida Fatima1

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

Computers in Biology and Medicine
|May 8, 2025
PubMed
Summary

This study introduces a hybrid Fuzzy Quantum Machine Learning (FQML) model to improve quantum machine learning (QML) performance. FQML effectively reduces computational complexity in high-dimensional data for better accuracy in medical predictions.

Keywords:
Chronic diseaseFuzzy K-Nearest neighbor (FQKNN)Fuzzy quantum machine learning (FQML)Fuzzy quantum support vector machine (FQSVM)Healthcare dataset

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Quantum machine learning (QML) shows promise but struggles with computational complexity in high-dimensional spaces.
  • Traditional machine learning (ML) also faces accuracy and computation time issues with large, high-dimensional datasets.

Purpose of the Study:

  • To develop a novel hybrid model, Fuzzy Quantum Machine Learning (FQML), to address the limitations of QML in high-dimensional data.
  • To enhance the accuracy and reduce computation time for medical datasets, specifically for chronic disease prediction.

Main Methods:

  • Integrated fuzzy logic (FL) concepts with quantum machine learning (QML) algorithms, including Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).
  • Applied the hybrid FQML model to a medicine dataset of chronic diseases to reduce dimensionality by fusing features.
  • Performed statistical analysis to compare FQML with traditional QML.

Main Results:

  • The FQML model significantly optimized computation time and improved accuracy compared to standard QML.
  • Feature reduction through fuzzy logic transformed high-dimensional spaces into lower-dimensional ones, enhancing model performance.
  • Statistical analysis confirmed a significant difference between FQML and QML, highlighting FQML's superiority.

Conclusions:

  • The FQML model effectively overcomes the computational complexity challenges of QML in high-dimensional spaces.
  • FQML enables the consideration of all necessary features for prediction without compromising computational efficiency, crucial for medical diagnosis.
  • This hybrid approach offers a robust solution for complex data analysis in fields like medical diagnostics.