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Karyotyping

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Updated: Sep 18, 2025

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

ChromoCheck: Predicting Postnatal Chromosomal Trisomy Cases Using a Support Vector Machine Learning Model.

Nabras Al-Mahrami¹, Nuha Al Jabri¹, Amal A W Sallam²

¹Medical Laboratory Sciences Program, Health Sciences, Oman College of Health Sciences, P.O. Box 3720, Muscat 112, Oman.

|June 26, 2025

Summary

This study introduces a machine learning model for predicting postnatal chromosomal trisomy. The support vector machine achieved high accuracy, demonstrating its potential to enhance cytogenetic diagnosis.

Keywords:

chromosome machine learning support vector machine trisomy

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Area of Science:

Genetics
Computational Biology
Medical Diagnostics

Background:

Karyotyping is a traditional method for identifying chromosomal abnormalities like trisomy.
Despite its historical significance, karyotyping has limitations in laboratory procedures.
Machine learning offers advanced predictive capabilities in medical diagnostics.

Purpose of the Study:

To develop and evaluate a machine learning model for predicting postnatal chromosomal trisomy.
To utilize a support vector machine (SVM) for this predictive task.
To assess the model's performance using key metrics and cross-validation.

Main Methods:

A dataset of 946 neonatal records from 2013-2023 was analyzed.
Thyroxine and thyroid-stimulating hormone levels were key features.
An SVM model with linear, radial, and polynomial kernels was tested using leave-one-out cross-validation.

Main Results:

The linear kernel demonstrated optimal classification performance with 81% training and 82% testing accuracy.
High sensitivity (97-98%) and specificity (79-80%) were achieved.
Area under the curve reached 0.89 for training and 0.90 for testing data.
The model was deployed as a web tool, ChromoCheck.

Conclusions:

Machine learning models can significantly augment traditional cytogenetic diagnostic methods.
The developed SVM model shows promise for clinical decision-making in trisomy prediction.
This approach offers a valuable tool for improving the efficiency and accuracy of genetic disorder diagnosis.