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.

随着精准医学和医疗保健计算的最新进展，对开发基因组学机器学习算法以增强疾病快速分析的需求巨大。基因组学和成像技术的进步为临床医生提供了大量数据，但预测仍然大多是主观的，导致医疗治疗出现问题。机器学习正在医疗保健领域的多个领域得到应用，包括临床研究、早期疾病识别和具有历史视角的医学创新。这项研究的主要目的是检测根据多种医学标准更容易患有遗传性疾病的患者。采用遗传病预测算法，利用患者的健康史来评估诊断遗传性疾病的可能性。我们开发了一种计算效率高的机器学习方法来预测基因组疾病患者的整体寿命，并对遗传性疾病患者进行分类和预测。使用两层元估计器堆叠 SVM、RF 和 ETC 以开发所提出的模型。第一层包括用于根据数据集预测结果的所有基线模型。第二层包括称为元分类器的组件。实验结果表明，该模型的准确率达到了90.45%，召回率达到了90.19%。线粒体疾病的曲线下面积 (AUC) 为 98.1%；对于多因素疾病，为97.5%；单基因遗传率为98.8%。所提出的方法提出了一种以公正、准确和全面的方式预测患者预后的新方法。所提出的方法在识别准确性方面优于使用当前遗传病分类临床标准的人类专业人员。 Stacked的实施将通过提高对遗传疾病的预测来显着改善生物医学研究领域。