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DNA sequence classification for diabetes mellitus using NuSVC and XGBoost: A comparative.

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XGBoost machine learning accurately predicts Diabetes Mellitus risk from DNA sequences, outperforming NuSVC. This advances early detection through genetic analysis for better diabetes management.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Precision Medicine

Background:

  • Diabetes Mellitus is a global health issue requiring early risk identification.
  • Traditional diagnostics miss genetic predispositions.
  • Genomic analysis offers potential for identifying diabetes-related genetic markers.

Purpose of the Study:

  • To classify DNA sequences for predicting Diabetes Mellitus susceptibility.
  • To compare the performance of NuSVC and XGBoost machine learning models for this task.

Main Methods:

  • Utilized NLP techniques to process DNA sequences as text, converting them to numerical features via TF-IDF.
  • Applied SMOTE to address class imbalance in the dataset.
  • Trained and validated NuSVC and XGBoost models using 10-fold cross-validation.

Main Results:

  • XGBoost achieved 98% accuracy, 0.0650 log loss, and 1.00 AUC.
  • NuSVC achieved 87% accuracy, 0.2649 log loss, and 0.95 AUC.
  • XGBoost demonstrated superior performance across all evaluated metrics.

Conclusions:

  • XGBoost is robust for analyzing complex genetic data in Diabetes Mellitus prediction.
  • Machine learning, particularly XGBoost, shows significant potential for early diabetes diagnosis.
  • Integrating advanced ML models into healthcare can enhance predictive diagnostics for diabetes.