Grouped-sampling technique to deal with unbalance in Raman spectral data modeling

Song, Haitao; Leng, Hongyong; Hou, Zhuoya; Gao, Rui; Chen, Cheng; Meng, Chunzhi; Sun, Jinshan; Li, Chenxi; Ma, Binlin

doi:10.1016/j.pdpdt.2022.103059

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…This poses a difficulty for learning algorithms, as they will be biased toward the majority group. [12][13][14] At the same time, the minority class is the item with more significance from the data mining perspective, and despite its rareness, it may carry important and useful knowledge. [15] Surface-enhanced Raman spectroscopy (SERS) has shown highly promising for existing cancer screening.…”

Section: Introductionmentioning

confidence: 99%

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Pan

Peng

Chen

et al. 2023

Advanced Intelligent Systems

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Surface‐enhanced Raman spectroscopy (SERS) has shown highly promising for existing cancer screening. However, previous “proof‐of‐concept” 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‐law‐based synthetic minority oversampling technique (PL‐SMOTE) method is proposed to guide the resampling of multiclass serum SERS data by analyzing the long‐tailed (power‐law) distribution of cancer prevalence in the population. The proposed PL‐SMOTE 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‐SMOTE verifies the effectiveness of proposed PL‐SMOTE method. After further fine‐tuning, the parameters of the deep neural network model and PL‐SMOTE method, an optimal cancer screening model with an optimal macroaveraged Recall score of 97.24% and an optimal macroaveraged F2‐Score 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.

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Section: Introductionmentioning

confidence: 99%

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Pan

Peng

Chen

et al. 2023

Advanced Intelligent Systems

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Modelling fish co-occurrence patterns in a small spring-fed river using a machine learning approach

Matsuzawa

Fukuda

Ohira

et al. 2023

Ecological Indicators

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Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation

Kim,

Yang,

Lee

2024

Applied Sciences

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In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive models is challenging due to privacy and data security concerns in integrating data from multiple emergency rooms. To address this, our work applies federated learning (FL) techniques, maintaining privacy without centralizing data. Medical data, which are often non-independent and identically distributed (non-IID), pose challenges for existing FL, where random client selection can impact overall FL performance. Therefore, we introduce a new client selection mechanism based on local model evaluation (LMECS), enhancing performance and practicality. This approach shows that the proposed FL model can achieve comparable performance to centralized models and maintain data privacy. The execution time was reduced by up to 27% compared to the existing FL algorithm. In addition, compared to the average performance of local models without FL, our LMECS improved the AUC by 2% and achieved up to 23% performance improvement compared to the existing FL algorithm. This work presents the potential for effective patient severity management in multi-institutional emergency rooms using FL without data movement, offering an innovative approach that satisfies both medical data privacy and efficient utilization.

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Grouped-sampling technique to deal with unbalance in Raman spectral data modeling

Cited by 3 publications

References 16 publications

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Modelling fish co-occurrence patterns in a small spring-fed river using a machine learning approach

Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation

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