Breast cancer recurrence time prediction based on the MOPSO-RF model

Background: Cancer is a complex disease where malignant tumors have high clinical incidence and mortality rates. A significant risk of postoperative recurrence also exists. This work was aimed at diagnosing the time to postoperative cancer recurrence which could help the patients. The post-operative breast cancer recovery data from Beijing Stre Cancer Data Analysis Joint Laboratory was utilized. The study was conducted to determine the weighting of adaptive symptoms regarding the time to breast cancer recurrence by selecting six indicators, i.e., immune, tumour, microenvironmental, psychological, nutritional and aerobic exercise and progressive work, involved in the mechanism of breast cancer recovery. Methods: A multi-objective particle swarm optimised random forest (MOPSO-RF) model for the adjuvant cancer diagnosis was introduced to predict the time to breast cancer recurrence. The random forest model was optimised to verify its accuracy. The multi-objective optimisation combined each indicator with time to the breast cancer recurrence to get an objective function for finding the optimal Pareto solution. Results: The results exhibited that the constructed model was effective in predicting the time to breast cancer recurrence and thus provided early warning indications in this regard. Conclusions: The model achieved 92.17% forecast accuracy compared to the Gradient Boosted Tree (GBDT), Support Vector Machine (SVM), Linear/Logistic Regression (LR), XGBoost and Random Forest (RF) algorithm models.

Multi-objective optimization; Particle swarm optimization; Random forest; Recurrence time; Assisted diagnosis; Cancer prediction

[1] Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: A Cancer Journal for Clinicians. 2022; 72: 7–33.

[2] Xia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chinese Medical Journal. 2022; 135: 584–590.

[3] Mohri M, Rostamizadeh A, Talwalkar A. Foundations of machine learning. MIT press: Cambridge. 2018.

[4] Ilyas QM, Ahmad M. An enhanced ensemble diagnosis of cervical cancer: a pursuit of machine intelligence towards sustainable health. IEEE Access. 2021; 9: 12374–12388.

[5] Lohani BP, Thirunavukkarasan M. A review: application of machine learning algorithm in medical diagnosis. 2021 International Conference on Technological Advancements and Innovations (ICTAI). Tashkent, 10–12 November 2021. IEEE: Tashkent, Uzbekistan. 2021.

[6] Waks AG, Winer EP. Breast cancer treatment. JAMA. 2019; 321: 288.

[7] Mohsin MY, Ali MR, Yousif M, Chaudhary ST, Tahir W, Wattoo WA. Accuracy improvement for the diagnosis of breast cancer using different techniques of machine learning. 2022 International Conference on Emerging Trends in Smart Technologies (ICETST). Karachi, 23–24 September 2022. IEEE: Karachi, Pakistan. 2022.

[8] Cheville A, Smith S, Barksdale T, Asher A. Cancer rehabilitation. In David X. Cifu (ed.) Braddom’s physical medicine and rehabilitation (pp. 568–593). 6th edn. Elsevier: Richmond. 2021.

[9] Ma Z, Zhang M, Liu J, Yang A, Li H, Wang J, et al. An assisted diagnosis model for cancer patients based on federated learning. Frontiers in Oncology. 2022; 12: 860532.

[10] Alghunaim S, Al-Baity HH. On the scalability of machine-learning algorithms for breast cancer prediction in big data context. IEEE Access. 2019; 7: 91535–91546.

[11] Yue W, Wang Z, Chen H, Payne A, Liu X. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018; 2: 13.

[12] Kaur M, Gianey HK, Singh D, Sabharwal M. Multi-objective differential evolution based random forest for e-health applications. Modern Physics Letters B. 2019; 33: 1950022.

[13] Qi C, Chen Q. Evolutionary random forest algorithms for predicting the maximum failure depth of open stope hangingwalls. IEEE Access. 2018; 6: 72808–72813.

[14] Adnan MN, Islam MZ. Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm. Knowledge-Based Systems. 2016; 110: 86–97.

[15] Kabiraj S, Raihan M, Alvi N, Afrin M, Akter L, Sohagi SA, et al. Breast cancer risk prediction using XGBoost and random forest algorithm. 2020 11th international conference on computing, communication and networking technologies (ICCCNT). Kharagpur, 01–03 July 2020. IEEE: Kharagpur, India. 2020.

[16] Yifan D, Jialin L, Boxi F. Forecast model of breast cancer diagnosis based on RF-AdaBoost. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). Beijing, 14–16 May 2021. IEEE: Beijing, China. 2021.

[17] Ranjan M, Shukla A, Soni K, Varma S, Kuliha M, Singh U. Cancer prediction using random forest and deep learning techniques. 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). Indore, 23–24 April 2022. IEEE: Indore, India. 2022.

[18] Huang Z, Chen D. A breast cancer diagnosis method based on VIM feature selection and hierarchical clustering random forest algorithm. IEEE Access. 2021; 10: 3284–3293.

[19] Yang AM, Han Y, Liu CS, Wu JH, Hua DB. D-TSVR recurrence prediction driven by medical big data in cancer. IEEE Transactions on Industrial Informatics. 2020; 17: 3508–3517.

[20] Arya N, Saha S. Multi-modal classification for human breast cancer prognosis prediction: proposal of deep-learning based stacked ensemble model. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2022; 19: 1032–1041.

[21] Kutrani H, Eltalhi S. Decision tree algorithms for predictive modeling in breast cancer treatment. 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). Sabratha, 23–25 May 2022. IEEE: Sabratha, Libya. 2022.

[22] Alkebsi K, Du W. A fast multi-objective particle swarm optimization algorithm based on a new archive updating mechanism. IEEE Access. 2020; 8: 124734–124754.

[23] Rajagopal A, Joshi GP, Ramachandran A, Subhalakshmi RT, Khari M, Jha S, et al. A deep learning model based on multi-objective particle swarm optimization for scene classification in unmanned aerial vehicles. IEEE Access. 2020; 8: 135383–135393.

[24] Juweid ME, Cheson BD. Positron-emission tomography and assessment of cancer therapy. New England Journal of Medicine. 2006; 354: 496–507.

[25] Knobf MT, Ferrucci LM, Cartmel B, Jones BA, Stevens D, Smith M, et al. Needs assessment of cancer survivors in Connecticut. Journal of Cancer Survivorship. 2012; 6: 1–10.

[26] Cromes Jr GF. Implementation of interdisciplinary cancer rehabilitation. Rehabilitation Counseling Bulletin. 1987; 21: 230–237.

[27] Pathak S, Mishra I, Swetapadma A. An assessment of decision tree based classification and regression algorithms. 2018 3rd International Conference on Inventive Computation Technologies (ICICT). Coimbatore, 15–16 November 2018. IEEE: Coimbatore, India. 2018.

[28] Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN’95-international conference on neural networks. Perth, 27 November 1995–01 December 1995. IEEE: Perth, Australia. 1995.

[29] Eberhart RC, Shi Y. Tracking and optimizing dynamic systems with particle swarms. Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546). Seoul, 27–30 May 2001. IEEE: Seoul, Korea (South). 2001.

[30] Bray F, Ferlay H, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2018; 68: 394–424.

[31] Mujahid AK, Thirumalai C. Pearson correlation coefficient analysis (PCCA) on adenoma carcinoma cancer. 2017 International Conference on Trends in Electronics and Informatics (ICEI). Tirunelveli, 11–12 May 2017. IEEE: Tirunelveli, India. 2017.

[32] Scherer P, Trębacz M, Simidjievski N, Viñas R, Shams Z, Terre HA, et al. Unsupervised construction of 558 computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics. 2022; 38: 1320–1327.