Genetic disorders and congenital abnormalities strategies for reducing the burden in the Region

Hamamy, Hanan; Alwan, Al

doi:10.26719/1997.3.1.123

Cited by 16 publications

(1 citation statement)

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“…About 100,000 children currently have syndrome B disorder while 12,000 people died from syndrome C disorder around the world [10]. Approximately 2% to 5% of all childbirths are diagnosed with genetic disorders [11] that may lead to 5% to 50% of deaths in the childhood [12]. The genome data contain useful information and several health-related indicators that can be used to predict the genetic disorder.…”

Section: Introductionmentioning

confidence: 99%

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

Raza

Rustam

Siddiqui

et al. 2022

Genes

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.

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Section: Introductionmentioning

confidence: 99%