scholarly journals Prediction of Cancer and Suggestion of Therapies

2020 ◽  
Author(s):  
Bhavani M ◽  
Pavithra V ◽  
Monesh R
Keyword(s):  
Attribute Selection ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Prediction System ◽  
Cancer Prediction ◽  
Mining Algorithm ◽  
Vector Machines ◽  
The Common ◽  
Genetic And Environmental Factors ◽  
Selection Algorithms

Cancer is becoming one among the common diseases in day to today life, determining cancer in an earlier stage is still problematic. Identification of genetic and environmental factors is necessary to predict the type of cancer. The idea is to develop a cancer prediction system that predict lung and oral cancer depending on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this study is to do a comparative analysis using different algorithms for cancer prediction and suggestion of therapy.

scholarly journals Prognosis of Cancer and Proposition of Therapeutics

2020 ◽  
Vol 9 (8) ◽  
pp. 394-397
Keyword(s):  
Random Forest ◽  
Genetic Factors ◽  
Attribute Selection ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Random Forest Algorithm ◽  
Cancer Prediction ◽  
Vector Machines ◽  
The Common ◽  
Selection Algorithms

Cancer is becoming one of the common diseases in day today life, identifying it in a prior stage is still difficult. Identification of environmental and genetic factors is necessary to predict the cancer. We developed a cancer prediction system to predict lung and oral cancer based on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this system is to predict the type of cancer and the suggested therapy using random forest algorithm.

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

Additive survival least square support vector machines: A simulation study and its application to cervical cancer prediction

2017 ◽  
Author(s):  
Chusnul Khotimah ◽  
Santi Wulan Purnami ◽  
Dedy Dwi Prastyo ◽  
Virasakdi Chosuvivatwong ◽  
Hutcha Sriplung
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machines ◽  
Simulation Study ◽  
Least Square ◽  
Support Vector ◽  
Cancer Prediction ◽  
Vector Machines

Language and Ideology in Congress

2011 ◽  
Vol 42 (1) ◽  
pp. 31-55 ◽  
Author(s):  
Daniel Diermeier ◽  
Jean-François Godbout ◽  
Bei Yu ◽  
Stefan Kaufmann
Keyword(s):  
Support Vector ◽  
Us Congress ◽  
Political Ideologies ◽  
Economic Interpretation ◽  
Vector Machines ◽  
The Us ◽  
Cent Level ◽  
The Common ◽  
Us Senate ◽  
Language And Ideology

Legislative speech records from the 101st to 108th Congresses of the US Senate are analysed to study political ideologies. A widely-used text classification algorithm – Support Vector Machines (SVM) – allows the extraction of terms that are most indicative of conservative and liberal positions in legislative speeches and the prediction of senators’ ideological positions, with a 92 per cent level of accuracy. Feature analysis identifies the terms associated with conservative and liberal ideologies. The results demonstrate that cultural references appear more important than economic references in distinguishing conservative from liberal congressional speeches, calling into question the common economic interpretation of ideological differences in the US Congress.

scholarly journals Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test

2021 ◽  
Vol 28 (5) ◽  
pp. 118-129
Author(s):  
Alabi Waheed Banjoko ◽  
◽  
Kawthar Opeyemi Abdulazeez ◽  
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Cross Validation ◽  
Classification Method ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Machine Method ◽  
Mining Algorithm ◽  
Splitting Ratio

Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.

scholarly journals Bio-molecular cancer prediction with random subspace ensembles of support vector machines

2005 ◽  
Vol 63 ◽  
pp. 535-539 ◽  
Author(s):  
Alberto Bertoni ◽  
Raffaella Folgieri ◽  
Giorgio Valentini
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Random Subspace ◽  
Cancer Prediction ◽  
Vector Machines

scholarly journals Analyzing Tuberculosis Reactivation in Patients with Rheumatoid Arthritis and Ankylosing Spondylitis Treated with Biological Therapy Using Machine Learning Methods

2021 ◽  
Vol 11 (23) ◽  
pp. 11400
Author(s):  
Andra-Maria Mircea-Vicoveanu ◽  
Elena Rezuș ◽  
Florin Leon ◽  
Silvia Curteanu
Keyword(s):  
Rheumatoid Arthritis ◽  
Ankylosing Spondylitis ◽  
Nearest Neighbor ◽  
Biological Therapy ◽  
Attribute Selection ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Correlation Based Feature Selection ◽  
Tuberculosis Reactivation ◽  
Selection Algorithms

This study is based on the consideration that the patients with rheumatoid arthritis and ankylosing spondylitis undergoing biological therapy have a higher risk of developing tuberculosis. The QuantiFERON-TB Gold test result was the output of the models and a series of features related to the patients and their treatments were chosen as inputs. A distribution of patients by gender and biological therapy, followed at the time of inclusion in the study, and at the end of the study, is made for both rheumatoid arthritis and ankylosing spondylitis. A series of classification algorithms (random forest, nearest neighbor, k-nearest neighbors, C4.5 decision trees, non-nested generalized exemplars, and support vector machines) and attribute selection algorithms (ReliefF, InfoGain, and correlation-based feature selection) were successfully applied. Useful information was obtained regarding the influence of biological and classical treatments on tuberculosis risk, and most of them agreed with medical studies.

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