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Tumor growth prediction and classification based on the KNN algorithm and discrete-time Markov chains (DTMC)

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Abstract

In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumors remains insufficient due to the intricate nature of this research domain. Therefore, the presence of any application able to predict the growth of the tumor may have a role in eliminating the tumor by finding the appropriate treatment for it before it grows. This paper investigates tumor growth and presents a technique for tumor growth prediction based on the Discrete Time Markov Chain (DTMC) and K-Nearest Neighbor (KNN) algorithms. The design and development of this technique consists of a proposition of a stochastic model of tumor progression. This is followed by an extension of the mode to several cases that allow the derivation of new cases based on the study of predictive probabilities. The aim of this paper is to develop a model based on the KNN and DTMC algorithms that can classify tumors and predict the future state based on the current state of the tumor without the knowledge of the past state. In other words, all relevant information about the past and the present that would be useful in making predictions is available in the current state. In terms of performance evaluation metrics, the results show that the proposed method exceeds the existing methods with 97.65% accuracy, 71.65% specificity and 99.087% sensitivity.

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Availability of data and materials

The publicly available data sets analyzed in this study, it can be found here: [https://wiki.cancerimagingarchive.net/display/Public/Brain-Tumor-Progression].

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Acknowledgements

The authors would like to thank El houcine El fatimi, Department of Computer Engineering, Ankara University, and Laila Boumlik, Department of Computer Engineering, Laval University, for their important comments while doing this research.

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This research received no external funding.

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  1. Department of Computer Engineering, Polytechnic School of Montreal, Montreal, QC, 2500H3T 1J4, Canada

    Lahcen El Fatimi & Hanifa Boucheneb

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  1. Lahcen El Fatimi
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  2. Hanifa Boucheneb
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Correspondence to Lahcen El Fatimi or Hanifa Boucheneb.

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El Fatimi, L., Boucheneb, H. Tumor growth prediction and classification based on the KNN algorithm and discrete-time Markov chains (DTMC). Neural Comput & Applic 35, 9739–9751 (2023). https://doi.org/10.1007/s00521-023-08212-w

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