Applying Data Mining Techniques to Medical Databases

The data mining techniques such as Neural Network, Naïve Bayes, and Association rules are at present not well explored on medical databases. In this paper, we present and analyze our experimental results on thrombosis medical database by employing data mining tool of XLMiner and using different data mining techniques such as Naive Bayes and Neural Network for classification, Association rules, and Neural Network and K-Nearest Neighbors for prediction. As seen from experiments, some results are common across various mining techniques while others are unique

Breast cancer prognosis for young patients

Background/Aims: Breast cancer (BCa) prognostication is a vital element for providing effective treatment for patients with BCa. Studies suggest that ethnicity plays a greater role in the incidence and poor prognosis of BCa in younger women than in their older counterparts. Therefore, the goal of this study was to assess the association between age and ethnicity on the overall final prognosis. Materials and Methods: Nottingham Prognostic Index (NPI) was used to analyze BCa prognosis using Howard University Cancer Center Tumor Registry and the National Cancer Institute\u27s Surveillance, Epidemiology, and End Results BCa datasets. Patients were grouped according to their predicted prognosis based on NPI scheme. Results: There was no correlation between the younger patients compared to their older counterparts for any of the prognostic clusters. The significance of ethnicity in poorer prognosis for younger age is not conclusive either. Conclusion: An extended prognostic tool/system needs to be evaluated for its usefulness in a clinical practice environment