Efficient Binary Classifier for Prediction of Diabetes Using Data Preprocessing and Support Vector Machine

被引：1

作者：

Pradhan, Madhavi ^{[1
]}

Bamnote, G. R. ^{[2
]}

机构：

[1] Univ Pune, AISSMS Coll Engn, Dept Comp Engn, Pune, Maharashtra, India

[2] SSGBAU, PRMIT&R, Dept Comp Sci & Engn, Amravati, Maharashtra, India

来源：

PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON FRONTIERS OF INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2014, VOL 1 | 2015年 / 327卷

关键词：

SVM; Diabetes; Classifier; Preprocessing; Binary Classifier; COMPONENT ANALYSIS; FEATURE-SELECTION; DIAGNOSIS; SYSTEM;

D O I：

10.1007/978-3-319-11933-5_15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Diabetes offer a sea of opportunity to build classifier as wealth of patient data is available in public domain. It is a disease which affects the vast population and hence cost a great deal of money. It spreads over the years to the other organs in body thus make its impact lethal. Thus, the physicians are interested in early and accurate detection of diabetes. This paper presents an efficient binary classifier for detection of diabetes using data preprocessing and Support Vector Machine (SVM). In this study, attribute evaluator and the best first search is used for reducing the number of features. The dimension of the input feature is reduced from eight to three. The dataset used is Pima diabetic dataset from UCI repository. The substantial increase is noted in accuracy by using the data pre processing.

引用

页码：131 / 140

页数：10

共 50 条

[21] Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier
Nayak, Munir Ahmad
Ghosh, Subimal
THEORETICAL AND APPLIED CLIMATOLOGY, 2013, 114 (3-4) : 583 - 603
[22] Preprocessing Unbalanced Data using Weighted Support Vector Machines for Prediction of Heart Disease in Children
Tavares, Thiago R.
Oliveira, Adriano L. I.
Cabral, George G.
Mattos, Sandra S.
Grigorio, Renata
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,
[23] Using support vector machine for prediction of machine degradation trend based on vibration data
Lin, Hui
Zuo, Ming J.
Proceedings of the First International Conference on Maintenance Engineering, 2006, : 283 - 291
[24] Extreme support vector machine classifier
Liu, Qiuge
He, Qing
Shi, Zhongzhi
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2008, 5012 : 222 - 233
[25] Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model
Liu, Ji-Long
Peng, Ying
Fu, Yong-Sheng
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2015, 16 (03): : 4774 - 4785
[26] Efficient Stock-Market Prediction Using Ensemble Support Vector Machine
Nti, Isaac Kofi
Adekoya, Adebayo Felix
Weyori, Benjamin Asubam
OPEN COMPUTER SCIENCE, 2020, 10 (01): : 153 - 163
[27] Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier
Shen, Yingran
Goparaju, Chandra
Yang, Yang
Babu, Benson A.
Gai, Weiming
Pass, Harvey
Jiang, Gening
TRANSLATIONAL LUNG CANCER RESEARCH, 2023, 12 (10) : 2055 - +
[28] The Construction of Support Vector Machine Classifier Using the Firefly Algorithm
Chao, Chih-Feng
Horng, Ming-Huwi
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2015, 2015
[29] Face Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier
Li, Rong-sheng
Lee, Fei-fei
Yan, Yan
Chen, Qiu
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: TECHNIQUES AND APPLICATIONS, AITA 2016, 2016, : 144 - 149
[30] Support Vector Regression for Classifier Prediction
Loiacono, Daniele
Marelli, Andrea
Lanzi, Pier Luca
GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, 2007, : 1806 - 1813

← 1 2 3 4 5 →