The role of clinical dashboards in improving nursing care: A systematic review

Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of ۷۸۳۴ records of breast cancer patients clinical and risk factors data. There were ۴۰۰۸ patients (۵۲.۴%) with breast cancers (malignant) and the remaining ۳۶۱۷ patients (۴۷.۶%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using ۲۰ fields (risk factor) of the database because database feature was restrictions. Used ۱۰-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of ۹۳%, specificity of ۹۳.۳۲%, sensitivity of ۹۵۰۵۶%, ROC of ۰.۹۵ and artificial neural network had accuracy of ۹۳.۲۳%, specificity of ۹۱.۹۸%, sensitivity of ۹۲.۶۹%, and ROC of ۰.۸.Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.

Keywords:

Breast Cancer , Data Mining , Classifiers