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A mathematical modelling to detect sickle cell anemia using Quantum graph theory and Aquila optimization classifier.

P Balamanikandan1, S Jeya Bharathi1

  • 1Department of Mathematics, Thiagarajar College of Engineering, Madurai, Tamilnadu, India.

Mathematical Biosciences and Engineering : MBE
|August 29, 2022
PubMed
Summary

Early identification of sickle cell anemia (SCA) is crucial. This study introduces a Quantum graph theory model and Long Short-Term Memory (LSTM) classifier for precise SCA detection, improving upon manual methods.

Keywords:
Aquila optimization based cascaded LSTM classifierQuantum graph theory modelelasticity property featuresgenetic disorderhemoglobinmonogenic disorderred blood cellssickle cell anemia

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Area of Science:

  • Computational Biology
  • Genetics
  • Data Mining

Background:

  • Sickle Cell Anemia (SCA) is a life-threatening monogenic disorder caused by β-globin gene mutations, leading to abnormal hemoglobin S and red blood cell sickling.
  • Early diagnosis of SCA is vital for timely treatment, but manual identification is time-consuming and prone to errors due to the vast number of red blood cells.
  • Data mining techniques offer a promising avenue for accurate and efficient SCA detection.

Purpose of the Study:

  • To develop a mathematical model using Quantum Graph Theory for extracting elasticity properties of red blood cells.
  • To accurately distinguish between normal red blood cells and those affected by Sickle Cell Anemia (SCA).
  • To enhance the precision and efficiency of SCA diagnosis through advanced computational methods.

Main Methods:

  • Input DNA sequences were processed using Quantum Graph Theory to extract elasticity features via spanning tree formation, graph construction, and hemoglobin quantization.
  • Extracted features were optimized using the Aquila optimization algorithm.
  • A cascaded Long Short-Term Memory (LSTM) classifier was employed for distinguishing between normal and SCA red blood cells.

Main Results:

  • The proposed Quantum Graph Theory model effectively extracted elasticity properties relevant to SCA identification.
  • The Aquila optimization and cascaded LSTM classifier achieved high accuracy, precision, sensitivity, specificity, and AUC in classifying SCA.
  • Performance metrics demonstrated the superiority of the proposed system compared to existing classifiers.

Conclusions:

  • The integration of Quantum Graph Theory, Aquila optimization, and LSTM provides a highly effective computational approach for early Sickle Cell Anemia detection.
  • This method offers a significant improvement over traditional manual diagnosis, reducing time and potential misclassification.
  • The validated effectiveness of this system holds promise for improved patient outcomes through earlier and more accurate SCA diagnosis.