scholarly journals Comparison of K nearest Neighbours and Support Vector Machine to Build a Breast Cancer Prediction Model

2021 ◽  
Vol 9 (5) ◽  
pp. 1435-1442
Author(s):  
Rachaell Nihalaani
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Support Vector ◽  
Cancer Prediction ◽  
Nearest Neighbours

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.

scholarly journals An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

2021 ◽  
Vol 11 ◽  
Author(s):  
Mozhi Wang ◽  
Zhiyuan Pang ◽  
Yusong Wang ◽  
Mingke Cui ◽  
Litong Yao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Immune System ◽  
T Cell ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Cancer Patients ◽  
Immune Status ◽  
Support Vector ◽  
Breast Cancer Patients

Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4+/CD8+ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3+CD8+ cytotoxic T cell percent; CD16+CD56+ NK cell absolute value; and CD3+CD4+ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.

scholarly journals Breast Cancer Prediction Using Machine Learning Algorithm with Big Data Concept

2020 ◽  
pp. 123-127
Author(s):  
R. Nirmalan ◽  
M. Javith Hussain Khan ◽  
V. Sounder ◽  
A. Manikkaraja
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Learning Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Cancer Prediction ◽  
Modern Computer ◽  
Huge Data

The evolution in modern computer technology produce an huge amount of data by the way of using updated technology world with the lot and lot of inventions. The algorithms which we used in machine-learning traditionally might not support the concept of big data. Here we have discussed and implemented the solution for the problem, while predicting breast cancer using big data. DNA methylation (DM) as well gene expression (GE) are the two types of data used for the prediction of breast cancer. The main objective is to classify individual data set in the separate manner. To achieve this main objective, we have used a platform Apache Spark. Here,we have applied three types of algorithms used for classification, they are decision tree, random forest algorithm, support vector machine algorithm which will be mentioned as SVM .These three types of algorithm used for producing models used for breast cancer prediction. Analyze have done for finding which algorithm will produce the better result with good accuracy and less error rate. Additionally, the platforms like Weka and Spark are compared, to find which will have the better performance while dealing with the huge data. The obtained outcome have proved that the Support Vector Machine classifier which is scalable might given the better performance than all other classifiers and it have achieved the lowest error range with the highest accuracy using GE data set

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 Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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