Rawla P. Epidemiology of prostate cancer. World J Oncol. 2019;10(2):63–89.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.
Chen R, Huang Y, Cai X, Xie L, He D, Zhou L, Xu C, Gao X, Ren S, Wang F, Ma L, Wei Q, Yin C, Tian Y, Sun Z, Fu Q, Ding Q, Zheng J, Ye Z, Ye D, Xu D, Hou J, Xu K, Yuan J, Gao X, Liu C, Pan T, Sun Y. and C. Chinese Prostate Cancer, Age-specific cutoff value for the application of percent free prostate-specific antigen (psa) in chinese men with serum psa levels of 4.0-10.0 ng/ml, PLoS One 10 (2015), no. 6, e0130308.
Wong LW, Mak SH, Goh BH, Lee WL. The convergence of Ftir and Evs: emergence strategy for non-invasive cancer markers discovery. Diagnostics (Basel). 2022;13:1.
Yap XL, Wood B, Ong TA, Lim J, Goh BH, Lee WL. Detection of prostate cancer via Ir spectroscopic analysis of urinary extracellular vesicles: A pilot study. Membr (Basel). 2021;11:8, 591.
Polo TCF, Miot HA. Use of Roc curves in clinical and experimental studies. J Vasc Bras 19 (2020), e20200186.
Kaur I, Doja MN, Ahmad T. Data mining and machine learning in cancer survival research: an overview and future recommendations. J Biomed Inf. 2022;128:104026.
Ma C, Wang L, Song D, Gao C, Jing L, Lu Y, Liu D, Man W, Yang K, Meng Z, Zhang H, Xue P, Zhang Y, Guo F, Wang G. Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: A retrospective study. BMC Med. 2023;21(1):198.
Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep. 2021;11(1):3254.
Chen RC, Dewi C, Huang SW, Caraka RE. Selecting critical features for data classification based on machine learning methods. J Big Data. 2020;7:52.
Suresh S, Newton DT, Everett THt, Lin G, Duerstock BS. Feature selection techniques for a machine learning model to detect autonomic dysreflexia. Front Neuroinform. 2022;16:901428.
Blagus R, Lusa L. Smote for high-dimensional class-imbalanced data. BMC Bioinformatics. 2013;14(1):106.
Song YY, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry. 2015;27(2):130–5.
Blockeel H, Devos L, Frénay B, Nanfack G, Nijssen S. Decision trees: from efficient prediction to responsible Ai. Front Artif Intell. 2023;6:1124553.
Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing. 2020;408:189–215.
Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines, IEEE Intelligent Systems and their Applications 13 (1998), no. 4, 18-28.
Patle A, Chouhan DS. Svm kernel functions for classification, 2013 International Conference on Advances in Technology and Engineering (ICATE), 2013, pp. 1-9.
Guo G, Wang H, Bell D, Bi Y, Greer K. KNN model-based approach in classification. In: Meersman R, Tari Z, Schmidt DC, editors. On the move to meaningful internet systems. Berlin, Springer Berlin Heidelberg, Heidelberg; 2003. p. 986–996.
Taunk K, De S, Verma S, Swetapadma A. A brief review of nearest neighbor algorithm for learning and classification, 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, p.^pp. 1255-1260.
Vikramkumar V, Trilochan. Bayes and naive bayes classifier, arXiv pre-print server (2014).
Yang FJ. An implementation of naive bayes classifier, 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018, p.^pp. 301-306.
Dvorácek J. [adenocarcinoma of the prostate]. Cas Lek Cesk. 1998;137:17, 515–21.
Chen X, Jin H, Wang D, Liu J, Qin Y, Zhang Y, Zhang Y, Xiang Q. Serum creatinine levels, traditional cardiovascular risk factors and 10-year cardiovascular risk in Chinese patients with hypertension. Front Endocrinol (Lausanne). 2023;14:1140093.
Hu X-M, Zhang S-R, Li M, Deng JD. Multimodal particle swarm optimization for feature selection. Appl Soft Comput. 2021;113:107887.
Wang S, Dai Y, Shen J, Xuan J. Research on expansion and classification of imbalanced data based on Smote algorithm. Sci Rep. 2021;11(1):24039.
Nadkarni P. Chapter 10 - core technologies: Data mining and big data, Clinical research computing, P. Nadkarni, editor, Academic Press, 2016, pp. 187-204.
Yao Y, Cui H, Liu Y, Li L, Zhang L, Chen X. Pmsvm: an optimized support vector machine classification algorithm based on Pca and multilevel grid search methods. Math Probl Eng. 2015;2015(1):320186.
Shobha G, Rangaswamy S. Chapter 8 - machine learning, Handbook of statistics, V. N. Gudivada and C. R. Rao, editors, vol. 38, Elsevier, 2018, pp. 197-228.
Ye J, Dobson S, McKeever S. Situation identification techniques in pervasive computing: A review. Pervasive Mob Comput. 2012;8(1):36–66.
Hicks SA, Strümke I, Thambawita V, Hammou M, Riegler MA, Halvorsen P, Parasa S. On evaluation metrics for medical applications of artificial intelligence. Sci Rep. 2022;12(1):5979.
Melo F. Area under the Roc curve. In: Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H, editors. Encyclopedia of systems biology. New York, NY: Springer New York; 2013. pp. 38–9.
Talari ACS, Martinez MAG, Movasaghi Z, Rehman S, Rehman IU. Advances in fourier transform infrared (ftir) spectroscopy of biological tissues. Appl Spectrosc Rev. 2017;52(5):456–506.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority Over-sampling technique. J Artif Intell Res. 2002;16:321–57.
Lumbreras B, Parker LA, Caballero-Romeu JP, Gómez-Pérez L, Puig-García M, López-Garrigós M, García N, Hernández-Aguado I. Variables associated with False-Positive PSA results: A cohort study with Real-World data. Cancers (Basel). 2022;15(1):261.
Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: A Cancer Journal for Clinicians 74 (2024), no. 3, 229-263.
Barber L, Gerke T, Markt SC, Peisch SF, Wilson KM, Ahearn T, Giovannucci E, Parmigiani G, Mucci LA. Family history of breast or prostate cancer and prostate cancer risk. Clin Cancer Res. 2018;24:23, 5910–7.
Nair-Shalliker V, Bang A, Egger S, Yu XQ, Chiam K, Steinberg J, Patel MI, Banks E, O’Connell DL, Armstrong BK, Smith DP. Family history, obesity, urological factors and diabetic medications and their associations with risk of prostate cancer diagnosis in a large prospective study. Br J Cancer. 2022;127(4):735–46.
Hamed MA, Wasinger V, Wang Q, Graham P, Malouf D, Bucci J, Li Y. Prostate cancer-derived extracellular vesicles metabolic biomarkers: emerging roles for diagnosis and prognosis. J Controlled Release. 2024;371:126–45.
Su KY, Lee WL. Fourier transform infrared spectroscopy as a cancer screening and diagnostic tool: A review and prospects. Cancers (Basel). 2020;12(1):115.
Dai X, Fang X, Ma Y, Xianyu J. Benign prostatic hyperplasia and the risk of prostate cancer and bladder cancer: A meta-analysis of observational studies. Med (Baltim). no. 2016;18:95. e3493.
Weinstein SJ, Mackrain K, Stolzenberg-Solomon RZ, Selhub J, Virtamo J, Albanes D. Serum creatinine and prostate cancer risk in a prospective study. Cancer Epidemiol Biomarkers Prev. 2009;18(10):2643–9.
Ang M, Borg M, O’Callaghan ME. For the South Australian prostate cancer clinical outcomes, Survival outcomes in men with a positive family history of prostate cancer: A registry based study. BMC Cancer. 2020;20(1):894.
Jochems SHJ, Fritz J, Häggström C, Järvholm B, Stattin P, Stocks T. Smoking and risk of prostate cancer and prostate cancer death: A pooled study. Eur Urol. 2023;83(5):422–31.
Skotland T, Ekroos K, Kauhanen D, Simolin H, Seierstad T, Berge V, Sandvig K, Llorente A. Molecular lipid species in urinary exosomes as potential prostate cancer biomarkers. Eur J Cancer. 2017;70:122–32.
Brzozowski JS, Jankowski H, Bond DR, McCague SB, Munro BR, Predebon MJ, Scarlett CJ, Skelding KA, Weidenhofer J. Lipidomic profiling of extracellular vesicles derived from prostate and prostate cancer cell lines. Lipids Health Dis. 2018;17(1):211.
Luo J-q, Yang T-w, Wu J, Lai H-h, Zou L-b, Chen W-b, Zhou X-m, Lv D-j, S.-r. Cen, Z.-n. Long, Y.-y. Mao, P.-x. Zheng, X.-h. Su, Z.-y. Xian, F.-p. Shu and X.-m., Mao. Exosomal pgam1 promotes prostate cancer angiogenesis and metastasis by interacting with actg1, Cell Death & Disease 14 (2023), no. 8, 502.
Liu J, Dong B, Qu W, Wang J, Xu Y, Yu S, Zhang X. Using clinical parameters to predict prostate cancer and reduce the unnecessary biopsy among patients with Psa in the Gray zone. Sci Rep. 2020;10(1):5157.
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