Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach.

ABSTRACT:

Background

The malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model. More specifically, this study aims to quantify an individual's susceptibility to the development of malaria by using risk scores obtained from the cumulative effects of SNPs, known as weighted genetic risk scores (wGRS).

Results

We proposed an SNP-based feature extraction algorithm that incorporates the susceptibilit

SUBMITTER: Tai KY 

PROVIDER: S-EPMC9358850 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature