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Predicting diabetes with multivariate analysis an innovative KNN-based classifier approach.

B V V Siva Prasad1, Sapna Gupta2, Naiwrita Borah2

  • 1Department of CSE (School of Engineering), Anurag University, Hyderabad, Telangana, India.

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Summary
This summary is machine-generated.

This study introduces an adaptable neuro-fuzzy inference K-Nearest Neighbourhood (AF-KNN) model for predicting diabetes risk using patient data. The AF-KNN method enhances prediction accuracy by optimizing the K-Nearest Neighbourhood algorithm.

Keywords:
Adaptable fuzzified K-nearest Neighbourhood (AF-KNN)Diabetic prognosisK-nearest Neighbourhood (KNN)Machine learning (ML) techniques

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Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Diabetes Mellitus Research

Background:

  • Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels.
  • Effective diabetes management requires accurate prognosis and risk assessment.
  • Handling sensitive patient data necessitates robust and reliable predictive models.

Purpose of the Study:

  • To develop an adaptable neuro-fuzzy inference K-Nearest Neighbourhood (AF-KNN) learning-dependent forecasting system.
  • To improve the prediction accuracy of diabetes risk assessment models.
  • To leverage patient behavioral traits for enhanced diabetes prognosis.

Main Methods:

  • Utilized the K-Nearest Neighbourhood (KNN) machine learning algorithm as a foundation.
  • Developed an adaptable neuro-fuzzy inference system (AF-KNN) integrated with KNN.
  • Optimized the proportion of neighborhoods within the KNN framework to minimize prediction inaccuracy.

Main Results:

  • The proposed AF-KNN system demonstrated improved predictive performance for diabetes risk.
  • The method effectively identified optimal neighborhood parameters for reduced inaccuracy.
  • Patient behavioral traits were successfully incorporated to enhance forecasting.

Conclusions:

  • The AF-KNN model offers a promising approach for accurate diabetes risk prediction.
  • This adaptable neuro-fuzzy system enhances the reliability of machine learning in clinical decision-making.
  • Optimizing KNN parameters is crucial for improving predictive accuracy in healthcare applications.