Abstract
With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient’s health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.
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Availability of data and material
The dataset we used in our study is freely available publicly on the Kaggle website: (https://www.kaggle.com/datasets/aryarishabh/of-genomes-and-genetics-hackerearth-ml-challenge).
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The authors are thankful to AIDA Lab CCIS Prince Sultan University, Riyadh, Saudi Arabia, for the support.
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A.R.: conceptualization, methodology. M.M.: software programming, validation, verification. T.S.: formal analysis, investigation. G.J.: resources, data curation, management.
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Rehman, A., Mujahid, M., Saba, T. et al. Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry. Funct Integr Genomics 24, 23 (2024). https://doi.org/10.1007/s10142-024-01289-z
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DOI: https://doi.org/10.1007/s10142-024-01289-z