Frontiers in Health Informatics www.ijmi.ir 2021; 10: 83 Open Access Copyright© 2021, Published by Frontiers in Health Informatics. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY) License (http://creativecommons.org/). Prediction breast cancer risk: Performance analysis data mining techniques Solmaz Sohrabi 1 * , Alireza Atashi 2 1 Department of Medical Informatics, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2 Department of E-Health, Virtual School, Tehran University of Medical Sciences, Medical Informatics Department, Breast Cancer Research Center, Motamed Cancer Institute (ACECR), Tehran, Iran Article Info A B S T R A C T Article type: Research 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 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-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 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8. 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. Article History: Received: 2021-04-21 Accepted: 2021-07-11 Published: 2021-07-25 * Corresponding author: Solmaz Sohrabi Department of Medical Informatics, Shahid Beheshti University of Medical Sciences, Tehran, Iran Email: elnazfatemi143@gmail.com Keywords: Breast Cancer Data Mining Classifiers Cite this paper as: Sohrabi S, Atashi A. Prediction breast cancer risk: Performance analysis data mining techniques. Front Health Inform. 2021; 10: 83. DOI: 10.30699/fhi.v10i1.296 INTRODUCTION In worldwide more than one million new instances of female bosom malignant growth are analyzed every year. Bosom malignancy presents genuine danger to the existences of individuals and it is the subsequent driving reason for death in ladies today and It is the most well-known reason for female demise in industrialized nations, the second most normal reason on the planet and the third generally regular in non-industrial nations [1-3]. Roughly 10–15% of patients with bosom malignancy bite the dust of disease metastasis or repeat, and early determination of it can improve guess. Death pace of ladies due to bosom malignant growth can be diminished if can be distinguished at a generally beginning phase. Early forecast of bosom disease assumes a basic part in fruitful treatment and saving existences of thousands of patients consistently. Information mining has become a basic approach for processing applications in the space of medication [4-6]. With the assistance of most recent, proficient and early showing techniques, most of such malignant growths are analyzed when the sickness is still at a restricted