scholarly journals Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260681
Author(s):  
Yongha Son ◽  
Kyoohyung Han ◽  
Yong Seok Lee ◽  
Jonghan Yu ◽  
Young-Hyuck Im ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Homomorphic Encryption ◽  
Cancer Recurrence ◽  
Personal Data ◽  
Privacy Preserving ◽  
Medical Data ◽  
Breast Cancer Recurrence ◽  
Inference Model ◽  
Cancer Data ◽  
Recurrence Prediction

Protecting patients’ privacy is one of the most important tasks when developing medical artificial intelligence models since medical data is the most sensitive personal data. To overcome this privacy protection issue, diverse privacy-preserving methods have been proposed. We proposed a novel method for privacy-preserving Gated Recurrent Unit (GRU) inference model using privacy enhancing technologies including homomorphic encryption and secure two party computation. The proposed privacy-preserving GRU inference model validated on breast cancer recurrence prediction with 13,117 patients’ medical data. Our method gives reliable prediction result (0.893 accuracy) compared to the normal GRU model (0.895 accuracy). Unlike other previous works, the experiment on real breast cancer data yields almost identical results for privacy-preserving and conventional cases. We also implement our algorithm to shows the realistic end-to-end encrypted breast cancer recurrence prediction.

scholarly journals Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

2020 ◽  
Vol 19 ◽  
pp. 117693512091795
Author(s):  
Zeinab Sajjadnia ◽  
Raof Khayami ◽  
Mohammad Reza Moosavi
Keyword(s):  
Breast Cancer ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Classification Algorithms ◽  
Data Preparation ◽  
Recurrence Prediction ◽  
Null Values ◽  
Error Removal ◽  
The Impact ◽  
F Measure

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

scholarly journals Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning

Open Medicine ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. 754-768
Author(s):  
Pei-Tse Yang ◽  
Wen-Shuo Wu ◽  
Chia-Chun Wu ◽  
Yi-Nuo Shih ◽  
Chung-Ho Hsieh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Class Imbalance ◽  
Cancer Recurrence ◽  
Ensemble Methods ◽  
High Sensitivity ◽  
Breast Cancer Recurrence ◽  
Breast Cancer Patients ◽  
Medical Treatments ◽  
Cost Sensitive Learning ◽  
Recurrence Prediction

Abstract Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effects, and even death. The prediction of breast cancer recurrence is crucial for reducing mortality. This paper proposes a prediction model for the recurrence of breast cancer based on clinical nominal and numeric features. In this study, our data consist of 1,061 patients from Breast Cancer Registry from Shin Kong Wu Ho-Su Memorial Hospital between 2011 and 2016, in which 37 records are denoted as breast cancer recurrence. Each record has 85 features. Our approach consists of three stages. First, we perform data preprocessing and feature selection techniques to consolidate the dataset. Among all features, six features are identified for further processing in the following stages. Next, we apply resampling techniques to resolve the issue of class imbalance. Finally, we construct two classifiers, AdaBoost and cost-sensitive learning, to predict the risk of recurrence and carry out the performance evaluation in three-fold cross-validation. By applying the AdaBoost method, we achieve accuracy of 0.973 and sensitivity of 0.675. By combining the AdaBoost and cost-sensitive method of our model, we achieve a reasonable accuracy of 0.468 and substantially high sensitivity of 0.947 which guarantee almost no false dismissal. Our model can be used as a supporting tool in the setting and evaluation of the follow-up visit for early intervention and more advanced treatments to lower cancer mortality.

Breast Cancer Recurrence Prediction Using Random Forest Model

2018 ◽  
pp. 318-329 ◽  
Author(s):  
Tahsien Al-Quraishi ◽  
Jemal H. Abawajy ◽  
Morshed U. Chowdhury ◽  
Sutharshan Rajasegarar ◽  
Ahmad Shaker Abdalrada
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Recurrence ◽  
Random Forest Model ◽  
Breast Cancer Recurrence ◽  
Forest Model ◽  
Recurrence Prediction
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