Abstract:
:Haemophilia is an X-linked genetic disorder in which A and B types are the most common that occur due to absence or lack of protein factors VIII and IX, respectively. Severity of the disease depends on mutation. Available Machine Learning (ML) methods that predict the mutational severity by using traditional encoding approaches, generally have high time complexity and compromised accuracy. In this study, Haemophilia 'A' patient mutation dataset containing 7784 mutations was processed by the proposed Position-Specific Mutation (PSM) and One-Hot Encoding (OHE) technique to predict the disease severity. The dataset processed by PSM and OHE methods was analyzed and trained for classification of mutation severity level using various ML algorithms. Surprisingly, PSM outperformed OHE, both in terms of time efficiency and accuracy, with training and prediction time improvement in the range of approximately 91 to 98% and 80 to 99% respectively. The severity prediction accuracy also improved by using PSM with different ML algorithms.
journal_name
Genomicsjournal_title
Genomicsauthors
Singh VK,Maurya NS,Mani A,Yadav RSdoi
10.1016/j.ygeno.2020.09.020subject
Has Abstractpub_date
2020-11-01 00:00:00pages
5122-5128issue
6eissn
0888-7543issn
1089-8646pii
S0888-7543(20)30819-3journal_volume
112pub_type
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