Predication of Parkinson′s disease using data mining methods: A comparative analysis of tree, statistical, and support vector machine classifiers

2011 ◽  
Vol 65 (6) ◽  
pp. 231 ◽  
Author(s):  
Yugal Kumar ◽  
Gadadhar Sahoo ◽  
Geeta Yadav
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Comparative Analysis ◽  
Support Vector ◽  
Mining Methods ◽  
Using Data

Analysis of Heart Disorder by Using Machine Learning Methods and Data Mining Techniques

2021 ◽  
pp. 212-221
Author(s):  
Sarangam Kodati ◽  
Jeeva Selvaraj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Heart Diseases ◽  
Support Vector ◽  
Primary Reason ◽  
The World ◽  
Mining Methods ◽  
Using Data

Data mining is the most famous knowledge extraction approach for knowledge discovery from data (KDD). Machine learning is used to enable a program to analyze data, recognize correlations, and make usage on insights to solve issues and/or enrich data and because of prediction. The chapter highlights the need for more research within the usage of robust data mining methods in imitation of help healthcare specialists between the diagnosis regarding heart diseases and other debilitating disease conditions. Heart disease is the primary reason of death of people in the world. Nearly 47% of death is caused by heart disease. The authors use algorithms including random forest, naïve Bayes, support vector machine to analyze heart disease. Accuracy on the prediction stage is high when using a greater number of attributes. The goal is to function predictive evaluation using data mining, using data mining to analyze heart disease, and show which methods are effective and efficient.

A Method for Classification Using Data Mining Technique for Diabetes

2016 ◽  
pp. 738-761
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Health Information System ◽  
Parameters Optimization ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Predictive Data Mining ◽  
Severity Of The Disease ◽  
Using Data

Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

Decision Support System for Diabetes Classification Using Data Mining Techniques

2021 ◽  
pp. 1091-1113
Author(s):  
Ahmad M. Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Predictive Data Mining ◽  
Severity Of The Disease ◽  
Using Data

The use of data mining algorithms in health information systems has played a significant role in developing applications that help to diagnose different diseases. The type of the disease determines the selection of the algorithm, parameters to be used, and dataset pre-processing steps, etc. In this chapter, diagnosing diabetes mellitus is the target since it has gained significant attention in the last few decades due to the increased severity of the disease. Four predictive data mining approaches are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset: k-nearest neighbor, support vector machine, multilayer perceptron neural network, and naive Bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

Decision Support System for Diabetes Classification Using Data Mining Techniques

2018 ◽  
pp. 281-303
Author(s):  
Ahmad M. Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Predictive Data Mining ◽  
Severity Of The Disease ◽  
Using Data

The use of data mining algorithms in health information systems has played a significant role in developing applications that help to diagnose different diseases. The type of the disease determines the selection of the algorithm, parameters to be used, and dataset pre-processing steps, etc. In this chapter, diagnosing diabetes mellitus is the target since it has gained significant attention in the last few decades due to the increased severity of the disease. Four predictive data mining approaches are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset: k-nearest neighbor, support vector machine, multilayer perceptron neural network, and naive Bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

A Method for Classification Using Data Mining Technique for Diabetes

2016 ◽  
pp. 426-449
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Health Information System ◽  
Parameters Optimization ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Predictive Data Mining ◽  
Severity Of The Disease ◽  
Using Data

Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

Early Breast Cancer Prediction Using Dermatoglyphics: Data Mining Pilot Study in a General Hospital in Iran (Preprint)

2020 ◽  
Author(s):  
Seyed Mohammad Ayyoubzadeh ◽  
Aysan Almasizand ◽  
Sharareh R. Niakan Kalhori ◽  
Sakineh Abbasi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Random Forest ◽  
Early Breast Cancer ◽  
Information Gain ◽  
Support Vector ◽  
Mining Methods ◽  
Using Data ◽  
Birth Family ◽  
Hands And Feet

BACKGROUND Dermatoglyphics 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 of breast cancer. There are several studies using data mining methods to evaluate the risk of breast cancer; while there is no or little study that evaluates finger patterns with data mining for breast cancer risk prediction. OBJECTIVE This study aims to evaluate fingerprint patterns along with other easy-to-obtain features in the risk of breast cancer. METHODS A dataset containing 462 records includes female patients in Imam Khomeini Hospital Complex, Tehran, Iran was obtained. The dataset has comprised of age, menstruation age, menopause age, and situation, has a child, age at first live birth, family history of breast cancer, and figure print patterns features of hands. 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 (RF), Support Vector Machine (SVM), and Deep Learning classifiers. RESULTS The most important factor determining breast cancer were age, having a child, menopause situation, and menopause age. The best performance belongs to the RF model with accuracy and AUC of 84.43% and 0.923 respectively. The fingerprint patterns feature increased the RF accuracy from 79.44% to 84.43%. CONCLUSIONS 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.

scholarly journals INTELLECTUAL ANALYSIS OF DATA

2019 ◽  
Vol 1 (92) ◽  
pp. 65-70
Author(s):  
G.V. Marchuk ◽  
V.L. Levkivskyy ◽  
S.S. Kaliberda
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Polynomial Regression ◽  
Support Vector ◽  
Main Research ◽  
Forecasting Models ◽  
Mining Methods ◽  
Hidden Patterns

The main research of the article is the data mining methods, such as linear and polynomial regression and the support vector machine. The application success is based on the fact that the methods and technologies of Data mining ensure the study of data and the research of hidden patterns in them. The analysis assists in identification of various features and data parameters, and therefore it is a powerful tool in the stage of forming forecasting models.

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