scholarly journals Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine

2012 ◽  
Vol 15 (2) ◽  
pp. 230 ◽  
Author(s):  
Woojae Kim ◽  
Ku Sang Kim ◽  
Jeong Eon Lee ◽  
Dong-Young Noh ◽  
Sung-Won Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Support Vector ◽  
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.

IMPROVING CLASSIFICATION ACCURACY FOR NON-COMMUNICABLE DISEASE PREDICTION MODEL BASED ON SUPPORT VECTOR MACHINE

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Mohd. Khanapi Abd. Ghani ◽  
Daniel Hartono Sutanto
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Communicable Disease ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Redundant Data ◽  
Non Communicable Disease

Over recent years, Non-communicable Disease (NCDs) is the high mortality rate in worldwide likely diabetes mellitus, cardiovascular diseases, liver and cancers. NCDs prediction model have problems such as redundant data, missing data, imbalance dataset and irrelevant attribute. This paper proposes a novel NCDs prediction model to improve accuracy. Our model comprisesk-means as clustering technique, Weight by SVM as feature selection technique and Support Vector Machine as classifier technique. The result shows that k-means + weight SVM + SVM improved the classification accuracy on most of all NCDs dataset (accuracy; AUC), likely Pima Indian Dataset (99.52; 0.999), Breast Cancer Diagnosis Dataset (98.85; 1.000), Breast Cancer Biopsy Dataset (97.71; 0.998), Colon Cancer (99.41; 1.000), ECG (98.33; 1.000), Liver Disorder (99.13; 0.998).The significant different performed by k-means + weight by SVM + SVM. In the time to come, we are expecting to better accuracy rate with another classifier such as Neural Network.

A prediction model for breast cancer recurrence after adjuvant hormone therapy

2008 ◽  
Vol 23 (4) ◽  
pp. 199-206 ◽  
Author(s):  
P. Boracchi ◽  
D. Coradini ◽  
S. Antolini ◽  
S. Oriana ◽  
Ruggero Dittadi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Hormone Therapy ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Adjuvant Hormone Therapy
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