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However, previous \u201cproof\u2010of\u2010concept\u201d studies ignored the natural imbalance of cancer types in the population, leading the model to be biased toward learning more features in majority class during the learning process at the expense of ignoring minority class. Herein, a power\u2010law\u2010based synthetic minority oversampling technique (PL\u2010SMOTE) method is proposed to guide the resampling of multiclass serum SERS data by analyzing the long\u2010tailed (power\u2010law) distribution of cancer prevalence in the population. The proposed PL\u2010SMOTE method balances the number of minorities to resample and the number of overlaps between classes by introducing modulating factor. Modeling on resampled datasets synthesized by PL\u2010SMOTE verifies the effectiveness of proposed PL\u2010SMOTE method. After further fine\u2010tuning, the parameters of the deep neural network model and PL\u2010SMOTE method, an optimal cancer screening model with an optimal macroaveraged Recall score of 97.24% and an optimal macroaveraged F2\u2010Score of 97.38% is obtained. A new method for multiclass imbalanced resampling is provided, which has significant improvement on model performance in terms of SERS cancer screening. The method also inspires in other multiclass imbalanced scenario, such as biological medicine, abnormal detection, and disaster prediction.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202300006","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T23:36:06Z","timestamp":1680219366000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Power\u2010Law\u2010Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface\u2010Enhanced Raman Spectroscopy Data for Cancer Screening"],"prefix":"10.1002","volume":"5","author":[{"given":"Changbin","family":"Pan","sequence":"first","affiliation":[{"name":"Key Laboratory of OptoElectronic Science and Technology for Medicine Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology Fujian Normal University  Fuzhou Fujian 350117 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