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Cancer is a public health problem on a global scale due to its high fatality rate and general complexity. The advancement of cancer prediction based on gene expression has been hastened by the rapid development of modern high-throughput sequencing methods and different machine-learning algorithms, offering insights into effective and precise treatment decision-making. Therefore, it is crucial to create Machine Learning (ML) algorithms that can tell cancer patients apart from healthy individuals. No one classification method has emerged as particularly successful, despite the widespread use of classification methods for cancer prediction. Using a multi-machine learning model optimization strategy, this research demonstrates how Deep Learning (DL) can be utilized to increase the accuracy of the models. We have chosen potential informative genes using statistical analysis, and we have been training five different classification models with these genes. The data from the five distinct classifiers is then "ensembled" using a deep learning technique. The great majority of cases are lung, stomach, and breast adenocarcinomas. Due to this, we applied deep learning-based methods to test the suggested inter-ensembles model using data from the cancer field. According to the research findings, using more than one set of classifiers or the conventional consensus approach improves the accuracy of cancer prognosis. The suggested deep learning-based inter-ensemble technique has been demonstrated to be accurate and effective for cancer diagnosis employing a wide range of classifiers.
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