Volume 9, Issue 3 (2021)
Health Educ Health Promot 2021, 9(3): 279-285 |
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Ayyoubzadeh S, Almasizand A, Rostam Niakan Kalhori S, Baniasadi T, Abbasi S. Early Breast Cancer Prediction Using Dermatoglyphics: Data Mining Pilot Study in a General Hospital in Iran. Health Educ Health Promot 2021; 9 (3) :279-285
URL: http://hehp.modares.ac.ir/article-5-53673-en.html
URL: http://hehp.modares.ac.ir/article-5-53673-en.html
1- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- Department of Laboratory Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- Department of Health Information Technology, Faculty of Para-Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
4- Department of Laboratory Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,sakineh4612004@yahoo.com
2- Department of Laboratory Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- Department of Health Information Technology, Faculty of Para-Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
4- Department of Laboratory Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,
Abstract: (1713 Views)
Aims: Dermatoglyphic is the study of skin patterns on hands and feet. It has been shown in some studies that specific finger patterns could be a risk factor for breast cancer. Thus, this study aims to evaluate fingerprint patterns and other easy-to-obtain features in the risk of breast cancer.
Instrument & Methods: This descriptive study was conducted in 2020. A dataset containing 462 records included female patients in Imam Khomeini Hospital Complex, Tehran, Iran. The factors' weight was determined by the Information Gain index. Predictive models were built once without fingerprint features and once with fingerprint features using Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and Deep Learning classifiers. RapidMiner 9.7.1 Software was used.
Findings: The most important factor determining breast cancer were age, having a child, menopause situation, and menopause age. The best performance was the Random Forest model with accuracy and Area under Curve of a Receiver operating characteristic of 84.43% and 0.923, respectively. The fingerprint patterns feature increased the RF accuracy from 79.44% to 84.43%.
Instrument & Methods: This descriptive study was conducted in 2020. A dataset containing 462 records included female patients in Imam Khomeini Hospital Complex, Tehran, Iran. The factors' weight was determined by the Information Gain index. Predictive models were built once without fingerprint features and once with fingerprint features using Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and Deep Learning classifiers. RapidMiner 9.7.1 Software was used.
Findings: The most important factor determining breast cancer were age, having a child, menopause situation, and menopause age. The best performance was the Random Forest model with accuracy and Area under Curve of a Receiver operating characteristic of 84.43% and 0.923, respectively. The fingerprint patterns feature increased the RF accuracy from 79.44% to 84.43%.
Conclusion: An early breast cancer screening model could be built with the use of data mining methods. The fingerprint patterns could increase the performance of these models. The Random Forest model could be used. The results of such models could be used in designing apps for self-screening breast cancer.
Article Type: Descriptive & Survey |
Subject:
Technology of Health Education
Received: 2021/05/29 | Accepted: 2021/08/29 | Published: 2021/10/17
Received: 2021/05/29 | Accepted: 2021/08/29 | Published: 2021/10/17
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