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An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis.

Miaomiao Liu1, Tingyin Wang1, Qiyi Zhang1

  • 1Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350117, China. tywang@fjnu.edu.cn.

Analytical Methods : Advancing Methods and Applications
|January 17, 2024
PubMed
Summary

A new method, Principal Component Analysis and Density Based Spatial Clustering of Applications with Noise (PCA-DBSCAN), effectively removes outliers from cancer screening data. This improves the accuracy of surface-enhanced Raman spectroscopy (SERS) models for early cancer detection.

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Area of Science:

  • Spectroscopy
  • Data Science
  • Biomedical Engineering

Background:

  • Surface-enhanced Raman spectroscopy (SERS) shows potential for cancer screening.
  • Raman spectra can be affected by environmental factors and sample degradation, leading to outliers.
  • Existing methods often overlook outliers in categorical data, impacting model performance.

Purpose of the Study:

  • To introduce a novel outlier removal method for SERS cancer screening data.
  • To improve the accuracy and reliability of SERS-based cancer detection models.
  • To demonstrate the versatility of the proposed method in various scientific fields.

Main Methods:

  • Proposed Principal Component Analysis and Density Based Spatial Clustering of Applications with Noise (PCA-DBSCAN) for outlier detection.
  • Utilized dimensionality reduction and spectral data clustering to identify and remove outliers.
  • Optimized PCA-DBSCAN parameters (Eps, MinPts) and machine learning models for cancer screening.

Main Results:

  • The PCA-DBSCAN method effectively identified and removed outliers from SERS datasets.
  • Outlier removal significantly improved the performance of the cancer screening model.
  • Achieved a macro-average recall of 97.41% and a macro-average F1-score of 97.74% with the optimized model.

Conclusions:

  • PCA-DBSCAN is an effective method for outlier removal in SERS data, enhancing cancer screening accuracy.
  • The proposed method offers a significant improvement over previous approaches for SERS-based cancer detection.
  • The PCA-DBSCAN technique has broad applicability for outlier detection in diverse research and industrial domains.