Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

• Published in: Scientific reports Vol. 13; no. 1; p. 485
• Main Authors: Botlagunta, Mahendran, Botlagunta, Madhavi Devi, Myneni, Madhu Bala, Lakshmi, D., Nayyar, Anand, Gullapalli, Jaithra Sai, Shah, Mohd Asif
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.01.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67; 692/308; 692/4028; Accuracy; Algorithms; Blood; Breast cancer; Breast Neoplasms - diagnosis; Classification; Data mining; Data processing; Diagnosis; Electronic medical records; Female; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Malignancy; Melanoma, Cutaneous Malignant; Metastases; Monocytes; multidisciplinary; Neoplasms, Second Primary; Patients; Science; Science (multidisciplinary); Software; Statistical analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-27548-w

Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.