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Due to the high mortality of many cancers and their related diseases, the prediction and prognosis techniques of cancers are being extensively studied to assist doctors in making diagnoses. Many machine-learning-based cancer predictors have been put forward, but many of them have failed to become widely utilised due to some crucial problems. For example, most methods require too much training data, which is not always applicable to institutes, and the complicated genetic mutual effects of cancers are generally ignored in many proposed methods. Moreover, a majority of these assist models are actually not safe to use, as they are generally built on black-box machine learners that lack references from related field knowledge. We observe that few machine-learning-based cancer predictors are capable of employing prior knowledge (PrK) to mitigate these issues. Therefore, in this paper, we propose a generalisable informed machine learning architecture named the Informed Attentive Predictor (IAP) to make PrK available to the predictor's decision-making phases and apply it to the field of cancer prediction. Specifically, we make several implementations of the IAP and evaluate its performance on six TCGA datasets to demonstrate the effectiveness of our architecture as an assist system framework for actual clinical usage. The experimental results show a noticeable improvement in IAP models on accuracies, f1-scores and recall rates compared to their non-IAP counterparts (i.e., basic predictors). [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of death in the world. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we have proposed the Stacked Ensemble Model (Stacking of bagged and boosted learners) for the automatic disease diagnosis. The experimental results prove the superiority of the proposed method to conventional machine learning techniques. In the empirical study, the performance of eight data handling methods and 14 classification methods is compared to obtain prediction results. The performance of the model has been evaluated on five benchmark datasets. The appreciable Area under the Curve scores achieved by the proposed methodology on Cervical Cancer (0.98), Mesothelioma (0.93), Breast Cancer (0.99), Prostate Cancer (0.97), and Hepatitis‐C Virus (0.998) datasets make this work more significant than the previously published works. The experimental results show that our proposed method is superior to conventional machine learning techniques and the proposed model contributes in the form of an efficient computational model. [ABSTRACT FROM AUTHOR]