Using data mining techniques for diagnosis and prognosis of cancer disease (1205.1923v1)

Abstract: Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.

• The paper demonstrates that decision trees, particularly the C4.5 algorithm, achieve high accuracy in classifying breast cancer risk in case-control studies.
• It employs neural networks combined with association rule mining to improve the classification of digital mammograms, reaching over 70% accuracy.
• The study shows that SVMs, logistic regression, and Bayesian networks effectively predict prognosis, supporting personalized treatment strategies.

Data Mining Techniques for Breast Cancer Diagnosis and Prognosis

This paper provides a comprehensive analysis of data mining techniques applied to the diagnosis and prognosis of breast cancer. Given the public health burden of breast cancer, especially in developed regions including India, the efficacy of advanced computational techniques such as data mining in medical diagnostics has garnered significant research interest. The paper examines the utilization of several data mining methodologies, such as Decision Trees, Neural Networks, Association Rule Mining, Naïve Bayes, Support Vector Machines (SVMs), logistic regression, and Bayesian Networks, in the context of breast cancer analysis.

Methodological Approaches

The examination of data mining techniques offers a structured approach to understanding their application in breast cancer diagnosis and prognosis. Diagnosis focuses on distinguishing benign tumors from malignant ones, whereas prognosis involves predicting the recurrence of cancer in patients post-surgery.

1. Decision Trees: The paper discusses a paper leveraging decision trees generated using the C4.5 algorithm to classify breast cancer susceptibility. A case-control paper with Portuguese cases revealed high accuracy in identifying high-risk groups through permutation tests.
2. Neural Networks and Association Rule Mining: The use of neural networks combined with association rule mining strategies in image classification of digital mammograms is explored. The approach achieved a classification accuracy exceeding 70%, demonstrating the efficacy of combining machine learning with data mining for medical imaging.
3. Naïve Bayes: The authors present a comparison of Naïve Bayes with other techniques such as neural networks and C4.5 decision trees, finding close classification accuracy. These techniques were particularly applied for predicting patient survival, with the C4.5 exhibiting superior performance with an accuracy of 86.7%.
4. Support Vector Machines (SVMs): SVMs were used to analyze the predictive value of chemotherapy on survival time in breast cancer patients. By classifying patients into prognostic groups (Good, Poor, and Intermediate), SVMs provided insights into treatment efficacy, advocating chemotherapy for only specific prognostic groups.
5. Logistic Regression and Bayesian Networks: Logistic regression was evaluated alongside other classification models using the SEER database, achieving commendable accuracy, sensitivity, and specificity. Bayesian Networks were discussed as a robust model for integrating clinical and genomic data, optimizing prognosis predictions in lymph node-negative breast cancer cases.

Implications and Future Directions

This paper has significant implications for both clinical practice and future research. The quantitative analysis and comparison of various data mining techniques for breast cancer diagnosis and prognosis offer valuable insights for the deployment of automated diagnostic systems. Decision Trees exhibited the highest classification accuracy, suggesting potential for real-world integration into clinical settings, particularly in resource-limited areas. Additionally, Bayesian Networks provide a potent framework for prognosis predictions and could be further refined to incorporate a broader array of clinical data.

Future work is anticipated to extend these techniques into web-based applications, aiming at accessibility in remote or underserved regions. The continual evolution of data mining algorithms and the integration with heterogeneous clinical and genomic datasets could lead to enhanced predictive power and new diagnostic paradigms. The scope for further research lies in the adaptation of these models to larger and more diverse datasets, which could improve generalizability and robustness in varied clinical settings. As healthcare increasingly embraces digital tools, the potential to enhance early diagnosis and personal treatment plans with data-driven approaches will likely remain a pivotal research area.