Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

Eskandar Taghizadeh; Sahel Heydarheydari; Alihossein Saberi; Shabnam JafarpoorNesheli; Seyed Masoud Rezaeijo

doi:10.1186/s12859-022-04965-8

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

BMC Bioinformatics. 2022 Oct 1;23(1):410. doi: 10.1186/s12859-022-04965-8.

Authors

Eskandar Taghizadeh¹, Sahel Heydarheydari², Alihossein Saberi¹, Shabnam JafarpoorNesheli³, Seyed Masoud Rezaeijo⁴

Affiliations

¹ Department of Medical Genetic, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
² Department of Radiology Technology, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran.
³ Faculty of Engineering, University of Science and Culture, Tehran, Iran.
⁴ Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. rezaei-sm@ajums.ac.ir.

Abstract

Background: We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer.

Methods: In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used.

Results: Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66.

Conclusions: The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.

Keywords: Breast cancer; Feature selection; Machine learning; Prediction; Transcriptome profiling.

MeSH terms

Algorithms
Bayes Theorem
Breast Neoplasms* / diagnosis
Breast Neoplasms* / genetics
Early Detection of Cancer
Female
Gene Expression Profiling
Humans
Machine Learning
Support Vector Machine