Data Mining Technique for Medical Diagnosis Using a New Smooth Support Vector Machine

2010 ◽  
pp. 15-27 ◽  
Author(s):  
Santi Wulan Purnami ◽  
Jasni Mohamad Zain ◽  
Abdullah Embong
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Medical Diagnosis ◽  
Support Vector ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Smooth Support Vector Machine

scholarly journals Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

2019 ◽  
Vol 1255 ◽  
pp. 012067
Author(s):  
Natalina Br Sitepu ◽  
Sawaluddin ◽  
M Zarlis ◽  
Syahril Efendi ◽  
Hanna Willa Dhany
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Support Vector ◽  
Smooth Support Vector Machine

A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

2015 ◽  
Vol 10 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Information System ◽  
Classification Accuracy ◽  
Health Information System ◽  
Computer Applications ◽  
Parameters Optimization ◽  
Support Vector ◽  
Data Mining Technique ◽  
Predictive Data Mining

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%.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2014 ◽  
pp. 327-360 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

scholarly journals Development of a Data-Mining Technique for Regional-Scale Evaluation of Building Seismic Vulnerability

2019 ◽  
Vol 9 (7) ◽  
pp. 1502 ◽  
Author(s):  
Zhenyu Zhang ◽  
Ting-Yu Hsu ◽  
Hsi-Hsien Wei ◽  
Jieh-Haur Chen
Keyword(s):  
Data Mining ◽  
Computational Models ◽  
Seismic Vulnerability ◽  
Regional Scale ◽  
Support Vector ◽  
Data Mining Technique ◽  
Capacity Curves ◽  
Mining Technique ◽  
Svm Model ◽  
Building Characteristics

Assessing the seismic vulnerability of large numbers of buildings is an expensive and time-consuming task, requiring the collection of highly complex and multifaceted data on building characteristics and the use of sophisticated computational models. This study reports on the development of a data mining technique: Support Vector Machine (SVM) for resolving such multi-dimensional data problems for assessing buildings’ seismic vulnerability at a regional scale. Particularly, we developed an SVM model for rapid assessment of the macroscale seismic vulnerability of buildings in terms of spectral yield and ultimate points of their capacity curves. Two case studies, one with 11 building characteristics and the other with 20, were used to test the proposed SVM model. The results show that when 20 building characteristics are included, an individual building’s seismic vulnerability in term of its spectral yield and ultimate points can be predicted by the proposed SVM model with an average 64% accuracy if the training dataset contains 400 samples, rising to 74% with 4400 training samples. Coupling the proposed technique with demand curves based on buildings’ locations will enable rapid and reliable seismic-risk assessment at a regional scale, requiring only basic building characteristics rather than complex computational models.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2016 ◽  
pp. 923-954
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

scholarly journals Analysis of diabetic retinopathy using naive bayes classifier technique

2018 ◽  
Vol 7 (2.21) ◽  
pp. 440
Author(s):  
V Sudha ◽  
C Karthikeyan
Keyword(s):  
Data Mining ◽  
Diabetic Retinopathy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Plate Size ◽  
Retinal Vascular Disease ◽  
Two Stages

The vital issues of diabetes are Diabetic retinopathy (DR) and Retinal Vascular Disease which leads to the blindness. The DR disease may be detected by the early regular screening. and the automatic detection of this disease is a great solution and which is more reliable to identify the normality level in Fundus images (FI). The FI contains the texture discrimination capacity to differentiate the healthy images. The Data mining technique are used for identifying the retinal features of DR disease. The Data mining technique contains two stages. In first stage the features of DR disease extract from the Retinal Images (RI). The highlights for DR disease determination incorporate blood vessels, optic nerve, neural tissue, neuroretinal edge, optic plate size, thickness and change and which are removed by applying Data mining strategy. The result of the different information mining arrangement systems was looked at utilizing quick excavator apparatus. Gullible bayes and Support Vector Machine classifiers are utilized to anticipate the early discovery of eye disease diabetic retinopathy and observed that Naive bayes technique to be enhance the exactness of 89% precise.  

scholarly journals Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu

2021 ◽  
Vol 5 (2) ◽  
pp. 386-392
Author(s):  
Emy Haryatmi ◽  
Sheila Pramita Hervianti
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector ◽  
Time Data ◽  
Data Mining Technique ◽  
Student Data ◽  
Industry Standard ◽  
Svm Algorithm ◽  
Student Graduation ◽  
The Right

A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time.  

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Sign in / Sign up

Export Citation Format

Share Document