IRJET - A Survey on Machine Learning Intelligence Techniques for Medical Dataset Classsification
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This document discusses machine learning techniques for classifying medical datasets. It provides an overview of various artificial intelligence and machine learning algorithms that have been applied for medical dataset classification, including artificial neural networks, support vector machines, k-nearest neighbors, and decision trees. The document surveys works that have used these techniques for diseases like breast cancer, heart disease, and diabetes. It also describes common pre-processing steps for medical datasets like data normalization and feature selection methods like F-score and PCA that are used to select the most important features for classification. The classification algorithms are then evaluated based on accuracy metrics like sensitivity, specificity, and accuracy.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2336
Fig -2: Support Vectors
2.8 K-NN
K-NN is the non linear parameter used for classification and
regression. In both, for classificationandregressiontheinput
are combined with K closest neighbor values of the feature
space. Classificationisfollowedaccordingtotheclassification
condition. The output is assigned by membership class. The
value of K is either Positive or small. They classify the object
or feature according to the value.
2.9 PERFORMANCE ACCURACY
The Diagnosis of the diseases is identified by medical
datasets with several feature selection methods and
classification techniques. The diagnosis accuracy may be
varied from one exact technique to another technique.
Different feature extraction techniques that combine with
the classification method provide better results. TheTable 3
shows that the various accuracy levels of feature extraction
and classification methods during the emotionidentification
process.
Table -2: Accuracy of Methods
3. CONCLUSION
Classification of medical dataset can be identified by
extracting the different kind of featurefromthedatasets. For
extracting the features from the datasets Fuzzy C Means
Clustering will give the highestaccuracy,afterpreprocessing
the signal it has to be smoothed and optimized for the
particular feature, using different optimization techniques
like SVM, PSO, etc.. After getting the optimized result apply
the PSO and Fuzzy Cognitive map it will provide the high
accuracy of classification. These classification of medical
dataset will used for diagnosis of disease in early stage.
ACKNOWLEDGEMENT
We would earnestly thank our Supervisor forall hisvaluable
comments and active support.
REFERENCES
[1] Kemal Polat *, Salih Gunes ”A new feature selection
method on classification of medical datasets: Kernel F-score
feature selection” in 2009 Elsevier.
[2] P. Jaganathan and R. Kuppuchamy, “A threshold
fuzzy entropy based feature selection for medical database
classification,” Computers in Biology and Medicine, vol. 43,
no. 12, pp. 2222– 2229, 2013.
[3] Peng Tao1, Huang Yi” A Method Based on Weighted
F-score and SVM for Feature Selection” in 2015 CCDC.
[4] H. Temurtas, N. Yumusak, and F. Temurtas, “A
comparative study on diabetes disease diagnosis using
neural networks, Expert Systems with Applications, vol. 36,
no. 4, pp. 8610–8615, 2009.
[5] K. Polat, S.G¨unes¸, andA.Arslan, “Acascade learning
systemfor classification of diabetes disease: generalized
discriminant analysis and least square support vector
machine,” Expert Systems with Applications, vol. 34, no. 1,
pp. 482–487, 2008.
[6] M. F.Akay,“Supportvector machinescombinedwith
feature selection for breast cancer diagnosis,” Expert
Systems with Applications, vol. 36, no. 2, pp. 3240– 3247,
2009.
[7] S. Salcedo-Sanz, A. Pastor-S´anchez, L. Prieto, A.
Blanco- Aguilera, and RGarc´ıa-Herrera,“Featureselection in
wind speed prediction systems based on a hybridcoral reefs
optimization—extreme learningmachineapproach,”Energy
Conversion and Management, vol. 87, pp. 10–18, 2014
S. NO SELECTION
METHOD
CLASSIFICATION
ALGORITHM
ACCURACY CITIATION
1 Kernel F-
Score
SVM 76.03% [1]
2 Fuzzy
entropy
K-NN 75.45% [7]
3 F –Score NN 85.90% [3]
4 Weighted F-
Score
RBF 79.12% [8]
5 PCA SVM 67.8% [3,4]
6 GDA NN 70.8% [5]](https://image.slidesharecdn.com/irjet-v7i3465-201219042337/85/IRJET-A-Survey-on-Machine-Learning-Intelligence-Techniques-for-Medical-Dataset-Classsification-3-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2337
[8] J. Kennedy and R. C. Eberhart, “Particle swarm
optimization,” in Proceedings of the IEEE International
Conference on Neural Networks,vol.4,pp.1942–1948,IEEE,
Perth, Australia, December 1995.
[9] Kayaer, K., & Yıldırım, T. (2003). Medical diagnosis
on pima Indian diabetes using general regression neural
networks, artificial neural networks and neural information
processing(ICANN/ICONIP)(pp.181–184),Istanbul,Turkey,
June 26–29.
[10] Vapnik, V. (1995). The nature of statistical learning
theory. New York: Springer.
[11] Ster, B., & Dobnikar, A. (1996). Neural networks in
medical diagnosis: Comparison with other methods. In
Proceedings of the international conference on engineering
applications of neural networks (pp. 427–430).
[12] West, D.,Mangiameli,P.,Rampal,R.,&West,
V. (2005). Ensemble strategies for a medical diagnosis
decision support system: A breast cancer diagnosis
application. European Journal ofOperational Research(162),
532–551.
BIOGRAPHIES
P.M.Benson Mansingh completed
B.E (Electronics and
CommunicationEngineering)from
St.Peter’s engineering college in
2010. He received his Master’s
Degree in Network Engineering
from Anna University Regional
Campus, Coimbatore in 2014.
Currently he is pursuing his Ph.D
degree in ANNA UNIVERSITY,
Chennai and working as Assistant
Professor in Sri Ramakrishna
Institute of Technology.
Dr.M.Yuvaraju,AssistantProfessor,
Department of Electrical &
Electronics Engineering, Anna
University Regional Centre
Coimbatore. He has guided several
UG and PG students and currently
guiding 5 Ph.D Research Scholars
in Anna university, Chennai.
hoto](https://image.slidesharecdn.com/irjet-v7i3465-201219042337/85/IRJET-A-Survey-on-Machine-Learning-Intelligence-Techniques-for-Medical-Dataset-Classsification-4-320.jpg)
IRJET - A Survey on Machine Learning Intelligence Techniques for Medical Dataset Classsification
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2334 A SURVEY ON MACHINE LEARNING INTELLIGENCE TECHNIQUES FOR MEDICAL DATASET CLASSSIFICATION P.M. Benson Mansingh1, Dr. M. Yuvaraju2 1Assistant Professor, Dept of ECE, Sri Ramakrishna Institute of Technology, Coimbatore 2Assistant Professor, Dept of EEE, Anna University Regional Campus, Coimbatore ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - In this paper, a survey has been done on the application of Artificial intelligence computing techniquesfor diagnostic of disease by classifying the bio medical datasets. Many Artificial Intelligence techniques were reviewed for medical dataset classification. This Exploration assembles typical work that shows how the Artificial Neural Network is applied to the solution of different diagnostic disease with classification. It also detects themethodsandthetechniquesof ANN that are used frequently to solve the special problem related to the medicaldataset classification. ExtremeLearning Machine (ELM) is used in almost for learning the medical datasets to the network. Similarly PSO is used to optimize the attributes or the parameter of the datasets for classification. Several diseases like Breast cancer, Heart disease, Diabetes….etc using ANN approach are result in use of SVM (Support vector Machine) and BP network. Key Words: Medical datasets, Machine learning, Artificial Intelligence (AI), Extreme learning Machine(ELM),Artificial Neural Network (ANN) 1. INTRODUCTION Artificial Intelligence (AI) is the science and engineering making intelligent machines, especiallyintelligentprograms. And diagnosis is followed withthedevelopmentofalgorithm and techniques that are able to determine whether the behaviour of a system is correct. The application of computational or machine intelligence in medical diagnosis is a new trend for medical dataset classification. Classification system can help to minimizing possible errors that can be done because of inexperienced experts. And also provide medical dataset to be examined in shorter time and in detail. One of the application areas of analyzing database and medical dataset classification is automated diagnostic systems. The aims of these studies are assisting to doctors in making diagnostic decision with a subject to assurethediagnosis aid accurately. In medical field, many researchers applying different technique to improve the accuracy for the given data, by accurate values give after classification to know the affected patient and improve the diagnosis. That the classification efficiency is allowed through Sensitivity, Specificity and Accuracy for classification functions. A good classifier should give hundred percent results for all the three. 2. MEDICAL DATASET Medical classification, or medical coding, is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnosesand procedureswithinthehealthcarerecord,such as the transcription of the physician's notes, laboratory results,radiologicresults,andothersourcesareusuallytaken from a variety of source. Medical classification systems are used for a variety of applications in medicine, public health and medical informatics, including: statistical analysis of diseases and therapeutic actions reimbursement; e.g.,basedondiagnosis- related groups knowledge- based and decision support systems direct surveillance of epidemic or pandemic outbreaks. The medical datasets are taken from the UCI machine repository. 2.1 MEDICAL DATASET ANLAYSIS METHODS Medical dataset analysis method is included with three important steps. First step includes dataset pre processing, second follows feature selection and final step include classifying the dataset. This all described in following sections. 2.2 DATASET PRE PROCESSING Pre processing the thousands of medical datasets are combined into one relational table, by pre processing it delete the mismatched data and also it removes the multivalued attribute. And it replaces the missing value by its mean, median and its standard deviation (SD). Table -1: Different medical datasets LIST OF DISEASES Number of Instances OF INSTANCES Number of Attributes OF ATTRIBUTES Number of Classes O Breast Cancer 699 10 2 Diabetes 1151 20 3 Lung Cancer 32 56 4 Heart 303 75 2 Hepatitis 155 19 2 Thyroid 7200 21 4 Breast Cancer 699 10 2
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2335 2.3 FEATURE SELECTION After normalizing the datasets, Feature selection method is used to get the most important features of the dataset. That the method mentions the significant feature before going to classification. Several methods are used for feature selection are F-Score, Threshold fuzzy entropy, PCA, GDA etc. F-SCORE Featureselection byF-Scoreisusedtofindtheoptimalsubset of input variable with the best feature by removing no predictive information. That it gives the good accuracy value for classifying the medical datasets. It follows some steps for feature selection.. • All features are taken and calculated using the given formula. • Mean is calculated using the formula. • Compare the mean value of the feature with the original value. • It measures the discrimination or relevant feature values related to 2 different features. THRESHOLD FUZZY ENTROPY BASED FEATURE SELECTION Feature is selected based on the Fuzzy C means Clustering with three framework are followed • Mean selection • Half selection • Neural network 2.4 PCA PCA used to convert the set of observation of possibly correlated variable into the set of linearly uncorrelated values. And it reduces the dimension of the datasets with minimal loss of information and selects the most relevant feature. The reduction of feature dimension by extract the sub set of feature that describe as the best feature and evaluated with high accuracy. 2.5 GDA General Discriminant Analysis is used as a linear analysis model to the Discriminant analysis problem and also for classification problem. By using GDA we can set a complex model for the set of predictor variable. FEATURE CLASSIFICATION After the feature is extracted using the feature selection techniques. The best feature is selected using the classificationalgorithmslikeNeuralNetwork,SupportVector Machine (SVM), K-NN (K-Nearest Neighborhood) , Decision Tree. By using the following techniques the best feature are selected and grouped into one and classifying the datasets with high accurate of disease rate. That classification having both the supervised and unsupervised learning algorithms. Classification techniques forsupervised learning areCluster, Fuzzy C Means etc... And unsupervised learning algorithms are ANN, K-NN, Decision tree etc… 2.6 NEURAL NETWORK Artificial Neural Network is a powerful tool for solving the complex problem with linear input-output efficient relationship. That neural network is based on connections with human brain which havingNnumberofneurons.Neural network are followed with three layers they are input layer, output layer and hidden layer. Hidden layer is used to map the input function to the output. Neural network having following featuresitsupportseveralnetworkarchitecturefor supervised and unsupervised learning, it uses parallel computing for feature training process, dynamic network to store the data. And it uses unsupervised learning to train the new inputthatadjustsitselfcontinuously.Mathematicalrules like learning and training functions are used to adjust the weight and bias automatically. Fig -1: Neural Network 2.7 SVM Classification with SVM by finding the hyper plane that increases the dimensions of two plane classes. The hyper plane vector are defines support vectors.Maximizethewidth of the margin to get the optimalhyperplane.SVMareallowed with linear hyper plane, and then it have a unique global value. It built a model of new sample into one class, making a non probabilistic linear classifier.
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2336 Fig -2: Support Vectors 2.8 K-NN K-NN is the non linear parameter used for classification and regression. In both, for classificationandregressiontheinput are combined with K closest neighbor values of the feature space. Classificationisfollowedaccordingtotheclassification condition. The output is assigned by membership class. The value of K is either Positive or small. They classify the object or feature according to the value. 2.9 PERFORMANCE ACCURACY The Diagnosis of the diseases is identified by medical datasets with several feature selection methods and classification techniques. The diagnosis accuracy may be varied from one exact technique to another technique. Different feature extraction techniques that combine with the classification method provide better results. TheTable 3 shows that the various accuracy levels of feature extraction and classification methods during the emotionidentification process. Table -2: Accuracy of Methods 3. CONCLUSION Classification of medical dataset can be identified by extracting the different kind of featurefromthedatasets. For extracting the features from the datasets Fuzzy C Means Clustering will give the highestaccuracy,afterpreprocessing the signal it has to be smoothed and optimized for the particular feature, using different optimization techniques like SVM, PSO, etc.. After getting the optimized result apply the PSO and Fuzzy Cognitive map it will provide the high accuracy of classification. These classification of medical dataset will used for diagnosis of disease in early stage. ACKNOWLEDGEMENT We would earnestly thank our Supervisor forall hisvaluable comments and active support. REFERENCES [1] Kemal Polat *, Salih Gunes ”A new feature selection method on classification of medical datasets: Kernel F-score feature selection” in 2009 Elsevier. [2] P. Jaganathan and R. Kuppuchamy, “A threshold fuzzy entropy based feature selection for medical database classification,” Computers in Biology and Medicine, vol. 43, no. 12, pp. 2222– 2229, 2013. [3] Peng Tao1, Huang Yi” A Method Based on Weighted F-score and SVM for Feature Selection” in 2015 CCDC. [4] H. Temurtas, N. Yumusak, and F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks, Expert Systems with Applications, vol. 36, no. 4, pp. 8610–8615, 2009. [5] K. Polat, S.G¨unes¸, andA.Arslan, “Acascade learning systemfor classification of diabetes disease: generalized discriminant analysis and least square support vector machine,” Expert Systems with Applications, vol. 34, no. 1, pp. 482–487, 2008. [6] M. F.Akay,“Supportvector machinescombinedwith feature selection for breast cancer diagnosis,” Expert Systems with Applications, vol. 36, no. 2, pp. 3240– 3247, 2009. [7] S. Salcedo-Sanz, A. Pastor-S´anchez, L. Prieto, A. Blanco- Aguilera, and RGarc´ıa-Herrera,“Featureselection in wind speed prediction systems based on a hybridcoral reefs optimization—extreme learningmachineapproach,”Energy Conversion and Management, vol. 87, pp. 10–18, 2014 S. NO SELECTION METHOD CLASSIFICATION ALGORITHM ACCURACY CITIATION 1 Kernel F- Score SVM 76.03% [1] 2 Fuzzy entropy K-NN 75.45% [7] 3 F –Score NN 85.90% [3] 4 Weighted F- Score RBF 79.12% [8] 5 PCA SVM 67.8% [3,4] 6 GDA NN 70.8% [5]
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2337 [8] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks,vol.4,pp.1942–1948,IEEE, Perth, Australia, December 1995. [9] Kayaer, K., & Yıldırım, T. (2003). Medical diagnosis on pima Indian diabetes using general regression neural networks, artificial neural networks and neural information processing(ICANN/ICONIP)(pp.181–184),Istanbul,Turkey, June 26–29. [10] Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer. [11] Ster, B., & Dobnikar, A. (1996). Neural networks in medical diagnosis: Comparison with other methods. In Proceedings of the international conference on engineering applications of neural networks (pp. 427–430). [12] West, D.,Mangiameli,P.,Rampal,R.,&West, V. (2005). Ensemble strategies for a medical diagnosis decision support system: A breast cancer diagnosis application. European Journal ofOperational Research(162), 532–551. BIOGRAPHIES P.M.Benson Mansingh completed B.E (Electronics and CommunicationEngineering)from St.Peter’s engineering college in 2010. He received his Master’s Degree in Network Engineering from Anna University Regional Campus, Coimbatore in 2014. Currently he is pursuing his Ph.D degree in ANNA UNIVERSITY, Chennai and working as Assistant Professor in Sri Ramakrishna Institute of Technology. Dr.M.Yuvaraju,AssistantProfessor, Department of Electrical & Electronics Engineering, Anna University Regional Centre Coimbatore. He has guided several UG and PG students and currently guiding 5 Ph.D Research Scholars in Anna university, Chennai. hoto