Cancer prediction using graph-based gene selection and explainable classifier

Finnish Journal of eHealth and eWelfare

Mourad Oussalah¹  Mehrdad Rostami² 

[1] Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland;Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu;

关键词: artificial intelligence [http://www.yso.fi/onto/yso/p2616];    supervised machine learning;    medical informatics applications;    classification [http://www.yso.fi/onto/yso/p12668];    decision trees;   

DOI  :  10.23996/fjhw.111772

来源: DOAJ

【 摘 要 】

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time.

【 授权许可】

Unknown