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Updated: Jun 23, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Breast cancer symptom profile longitudinal changes: data mining study
Mohammad Fathian1, Farzane Akbari2
1Iran University of Science and Technology, Tehran, Iran fathian@iust.ac.ir.
Objectives:
Identifying stable co-occurring symptoms in breast cancer (BC) patients during chemotherapy can improve symptom management and the treatment process. This study examines symptom cluster stability in Iranian BC patients receiving chemotherapy and evaluates stability across three-time points within each cluster.
Methods:
This study collected data from three-time points: initial chemotherapy commencement, 2½ months postdiagnosis, and 5 months postdiagnosis. The research used exploratory factor analysis (EFA) in combination with hierarchical cluster analysis (HCA) and K means clustering to address research questions.
Results:
In the initial clustering step, EFA identified five clusters with high consistency across three-time points. The first cluster showed depression, anxiety and irritability, while the second cluster was characterised by sexual interest and pain. The third cluster was associated with diarrhoea and vomiting. In the second step, we obtained the HCA item output and two clusters of K means clustering that recorded depression and anxiety symptoms over time. Vomiting, dry mouth, sexual interest, worrying and numbness were observed during the first and second points, but the frequency has decreased since then.
Conclusions:
Cancer's psychological and physiological symptoms, including depression, anxiety, digestive and hormonal issues, remain stable throughout the disease. Palliative care centres can improve patients' quality of life and treatment process by addressing persistent symptoms.
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