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Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Machine Learning Algorithms
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  • Muhammad Junaid Anjum,
  • Fatima Tariq,
  • Khadeeja Anjum,
  • Hameed ur Rahman,
  • Faizan Ahmad,
  • Momina Shaheen
Muhammad Junaid Anjum
COMSATS University Islamabad - Lahore Campus
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Fatima Tariq
Lahore College for Women University
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Khadeeja Anjum
CMH Lahore Medical College and Institute of Dentistry
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Hameed ur Rahman
Air University
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Faizan Ahmad
Cardiff Metropolitan University School of Technologies
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Momina Shaheen
University of Roehampton School of Arts

Corresponding Author:[email protected]

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The production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation of every known gene related to a disease. This issue is further elevated when different variations of diseases are considered. As such the use of different computational methods are considered to tackle this issue. This research makes use of different machine learning algorithms in the identification and prediction of Single Nucleotide Polymorphism. This research presents that Gradient Boosting algorithm performs better in comparison to other algorithms in genic variation predictions with an accuracy of 70%.