Analytical Study of Diagnosis for Angioplasty and Stents Patients using Improved Classification...

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Analytical Study Stents Patients using Improved Classification Technique

Faisal Riyaz Wani1M.Tech

Department of Computer Sc. & Near NIT, Jalandhar

ABSTRACT The various technologies of data mining (DM) models for forecast of heart disease are discussed. Data mining plays an important role in building an intelligent model for medical systems to dedisease (HD) using data sets of Angioplasty and Stents patients, which involves risk factor associated with heart disease. Medical practitioners can help the patients by predicting the heart disease before occurring. The large data available frodiagnosis is analyzed by using data mining tools and useful information known as knowledge is extracted. Mining is a method of exploring massive sets of data to take out patterns which are hidden and previously unknown relationships and knowledgehelp the better understanding of medical data to prevent heart disease. There are many DM techniques available namely Classification techniques involving Naïve bayes (NB), Decision tree (DT), Neural network (NN), Genetic algorithm (GA), Artifintelligence (AI) and Clustering algorithms like KNN, and Support vector machine (SVM). Several studies have been carried out for developing prediction model using individual technique and also by combining two or more techniques. This research proviand easy review and understanding of available prediction models using data mining. The comparison shows the accuracy level of each model given by different researchers. Key Words: DM, ICA, OneR, ZeroR TRENDS IN USING DATAMINING TECHNIQUES IN HEART DISEASEAlthough applying data mining is beneficial to healthcare, disease diagnosis, and treatment, few researches have investigated producing treatment

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ISSN No: 2456 - 6470 | Volume - 3 | Issue – 1 | Nov

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Analytical Study of Diagnosis for Angioplasty sing Improved Classification Technique

Faisal Riyaz Wani1, Er. Harwinder Kaur 2 M.Tech CSE, 2Assistant Professor

Department of Computer Sc. & Engineering, St. Soldier Institute of EngineeringNear NIT, Jalandhar, Punjab, India

The various technologies of data mining (DM) models for forecast of heart disease are discussed. Data mining plays an important role in building an intelligent model for medical systems to detect heart disease (HD) using data sets of Angioplasty and Stents patients, which involves risk factor associated with heart disease. Medical practitioners can help the patients by predicting the heart disease before occurring. The large data available from medical diagnosis is analyzed by using data mining tools and useful information known as knowledge is extracted. Mining is a method of exploring massive sets of data to take out patterns which are hidden and previously unknown relationships and knowledge detection to help the better understanding of medical data to prevent heart disease. There are many DM techniques available namely Classification techniques involving Naïve bayes (NB), Decision tree (DT), Neural network (NN), Genetic algorithm (GA), Artificial intelligence (AI) and Clustering algorithms like KNN, and Support vector machine (SVM). Several studies have been carried out for developing prediction model using individual technique and also by combining two or more techniques. This research provides a quick and easy review and understanding of available prediction models using data mining. The comparison shows the accuracy level of each model given by

DATAMINING IN HEART DISEASE

Although applying data mining is beneficial to , disease diagnosis, and treatment, few

researches have investigated producing treatment

plans for patients. Accurate diagnosis and treatment given to patients have been major issues highlighted in medical services. Recently, researchers started investigating using data mining techniques to handle the error and complexity of healthcare providers. Razali and Aligenerating treatment plans for acute upperinfection disease patients using a decision tree. Themodel recommended treatment through giving drugs to patients showing accuracy of 94.73%. Applying association rules and decision tree to treatment plans are showing acceptable performance. However, the comparison with other data miningnaïve bayes, neural network, and geneticstill needs investigation. DATA MINING IN PREDICTION OF HEART DISEASE Despite the fact that data mining has more than one decade, its potenrealized now. Data mining join factual examination, machine learning and database hidden patterns and connections from substantial databases Data mining mainly uses twomethodologies: supervised and unsupervised learning’s. In learning, a training set is utilized to parameters though in unsupervised training set is utilized like in Ktwo most normal modeling goals classification and prediction. Classification models classify discrete unordered values or data whereas prediction models predicts about continuous valued. Decision trees and Neural Networks are examples of classification models while R

Research and Development (IJTSRD)

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1 | Nov – Dec 2018

Dec 2018 Page: 1170

Angioplasty and sing Improved Classification Technique

Engineering & Technology,

plans for patients. Accurate diagnosis and treatment have been major issues highlighted

Recently, researchers started mining techniques to handle

treatment processes for healthcare providers. Razali and Ali investigated

eatment plans for acute upper respiratory infection disease patients using a decision tree. The model recommended treatment through giving drugs

showing accuracy of 94.73%. Applying association rules and decision tree to treatment plans

performance. However, the comparison with other data mining techniques such as naïve bayes, neural network, and genetic algorithms

DATA MINING IN PREDICTION OF HEART

pite the fact that data mining has been around for more than one decade, its potential is just been

join factual examination, and database engineering to extract

connections from substantial Data mining mainly uses two

learning’s. In supervised t is utilized to learn model

n unsupervised learning no like in K-Means clustering. The

deling goals of data mining are nd prediction. Classification models

unordered values or data whereas prediction models predicts about continuous valued. Decision trees and Neural Networks are examples of classification models while Regression, Association

International Journal of Trend in Scientific Research and Development


Rules and Clustering are examples of prediction algorithm. In this prediction of heart disease, we will use the following classification models of data mining are analyzed: A. Decision trees B. Neural networks C. Naive Bayes Classifier PROPOSED SYSTEM To enhance the prediction of classifiers, genetic search is integrated; the genetic search resulted in 13 attributes which contributes more towards the prediction of the cardiac disease. The classifiers ensemble algorithms such as Random Forestfor prediction of patients with heart disease. Theclassifiers are fed with reduced data set with 13 attributes. Results are shown in. Observations exhibit that the Proposed classification technique outperforms other data mining techniques such as Naïve Bayes and Decision Tree after incorporating feature subset selection but with high model construction time. Proposed classification techniqueconsistently before and after reduction of attributes with the same model construction time. The proposed system will have the following features:� Collection of health care data of heart disease.� Storage and Processing of data using � Applying Proposed classification algorithm.� Analyzing performance in terms of

error rate. BJECTIVES OF THE PROPOSED STThe purpose of this research work to increase the efficiency of improved classification technique. The main purpose of this goal is to increase the efficiency and accuracy of the decision tree dataset.

Fig 1: The bar graph shows all attributes

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456


Rules and Clustering are examples of prediction

we will use the following classification models of data mining are

To enhance the prediction of classifiers, genetic integrated; the genetic search resulted in 13

contributes more towards the The classifiers

lgorithms such as Random Forest are used for prediction of patients with heart disease. The classifiers are fed with reduced data set with 13

Results are shown in. Observations exhibit technique outperforms

such as Naïve Bayes and incorporating feature subset

construction time. Proposed classification technique performs

and after reduction of attributes

The proposed system will have the following features: Collection of health care data of heart disease. Storage and Processing of data using Weka

algorithm. Analyzing performance in terms of accuracy and

TUDY purpose of this research work to increase the

efficiency of improved classification technique. The main purpose of this goal is to increase the efficiency

dataset.

1. To study the attribute of angioplasty and stent patients from various hospitals.

2. To refine the dataset for improving accuracy, correctly classified instance and reduce error rate.

3. To apply traditional and proposed algorithm on mixed dataset.

4. To analyze the computation parameters for improving efficiency of data set.

5. To critically analyze the diagnosis for angioplasty and stents patients using a improved classification technique.

6. To suggest self-programmedimproving efficiency of dec

This research work focuses on above stated objectives which aim in increase the efficiency on correctly classified instances and reducing error rate. Improved Classification AlgorithmThe Improved Classification Algorithmas below: Input : Dataset Output : Predicted class label set number of classes to N, number of features to set m determine the number of features at a node of decision tree, (m < M) for each decision tree do Select randomly, a subset (with rtraining data that represents the rest of data to measure the error of the treefor each node of this tree do Select randomly: m features to determine thedecision at the node and calculate the best split accordingly.end for end for

Results

: The bar graph shows all attributes

(IJTSRD) ISSN: 2456-6470

Dec 2018 Page: 1171

To study the attribute of angioplasty and stent patients from various hospitals. To refine the dataset for improving accuracy, correctly classified instance and reduce error rate. To apply traditional and proposed algorithm on

analyze the computation parameters for improving efficiency of data set. To critically analyze the diagnosis for angioplasty and stents patients using a improved classification

programmed algorithm for improving efficiency of decision tree technique.

research work focuses on above stated objectives im in increase the efficiency on correctly

classified instances and reducing error rate.

Algorithm Improved Classification Algorithm is explained

, number of features to M

determine the number of features at a node of

Select randomly, a subset (with replacement) of training data that represents the N classes and use the rest of data to measure the error of the tree

features to determine the

node and calculate the best split accordingly.



Fig 2 Shows the values of CCI, ICI and Error Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms

Fig 3: Shows the values of TP Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms

CONCLUSION Motivated by the world-wide increasing mortality of heart disease patients each year and the availability of huge amounts of data, researchers are using data mining techniques in the diagnosis of heart disease. Although applying data mining techniques to help health care professionals in the diagnosis ofdisease is having some success, the use of data miningtechniques to identify a suitable treatment for heart disease patients has received less attention. Also, applying hybrid data mining techniques has shown promising results in the diagnosis of heart disease, so applying improved data mining technique inthe suitable treatment for heart disease patients needsfurther investigation. This research identifies gaps in the research on heart disease diagnosis and treatment and proposes a model to systematically close those

OneR

CCI 80.28

ICI 19.71

ER 89.62

0

20

40

60

80

100

120

Va

lue

s

Comparison of CCI, ICI, ER

ICA

Positive 0.824

Negative 0.924

0

0.2

0.4

0.6

0.8

1

1.2

Un

its



CI and Error Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms

Shows the values of TP Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms

wide increasing mortality of disease patients each year and the availability of

of data, researchers are using data diagnosis of heart disease.

hniques to help health care professionals in the diagnosis of heart disease is having some success, the use of data mining techniques to identify a suitable treatment for heart

patients has received less attention. Also, techniques has shown

of heart disease, so data mining technique in selecting

the suitable treatment for heart disease patients needs identifies gaps in

on heart disease diagnosis and treatment to systematically close those

gaps to discover if applying dataheart disease treatment data can provideperformance as that achieved in diagnosing heartdisease patients. Improved Classification Algorithm (ICA) shows the better results in correctly classified instances, incorrect classified instances, error rate, TP Rate, FP Rate, Precision, Recall and F-Measure Values. REFERENCES 1. Aakash Chauhan, Aditya

Sharma, “Heart Disease Prediction using Evolutionary Rule Learning”, Conference on "Computational Intelligence and Communication Technology" (CICT 2018).

ZeroR NaiveBayes ICA

56.77 83.12 88.08

43.22 11.87 11.91

100 74.9 62.38

Comparison of CCI, ICI, ER

NaiveBayes ZeroR OneR

0.777 0.672 0

0.128 0.903 1

Analysis of TP Rate


Dec 2018 Page: 1172

CI and Error Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms

Shows the values of TP Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms

gaps to discover if applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart

Improved Classification Algorithm (ICA) shows the better results in correctly classified instances, incorrect classified instances, error rate, TP Rate, FP

Measure Values.

Aakash Chauhan, Aditya Jain, Purushottam Sharma, “Heart Disease Prediction using

Learning”, International Conference on "Computational Intelligence and Communication Technology" (CICT 2018).

88.08

11.91

62.38

OneR

0

1



2. M. Deepika, Dr. K. Kalaiselvi, “A Empirical study on Disease Diagnosis using Data Mining Techniques”, Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018)IEEE Xplore Compliant - Part Number: CFP18BAC-ART; ISBN:978-1-5386

3. Sarath Babu, Vivek EM, Famina KP, Fida K, Aswathi P, Shanid M, Hena M, “Diagnosis Using Data Mining International Conference on Electronics, Communication and Aerospace TechnologyICECA 2017.

4. Salma Banu N.K, Suma Swamy, “Heart Disease at early stage usingand Big Data Analytics: A Survey”,Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 2016.

5. Ankita Dewan, Meghna Sharma, “Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification”, 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015.

6. Monika Gandhi, Dr. Shailendra Narayan Singh, “Predictions in Heart Disease Usingof Data Mining”,1st International Conference on Futuristic trend in Computational Analysis and Knowledge Management (ABLAZE

7. M. A. JABBAR, B.L Deekshatulu, Priti Chndra, “Alternating decision trees for early diagnosis ofheart disease”, Proceedings of Conference on Circuits, Communication, Control and Computing (I4C 2014).

8. M. A. Jabbar, Shirina samreen, “prediction system based on hiddenclassifier”, GJCST, Volume 13 Issue 315-25,(2013).

9. Shubpreet Kaur, Dr. R.K.Bawa, “Implementation of an Expert System for the Identification of Drug Addiction Using Decision Tree ID3 Algorithm”, IEEE, 2017.

10. Priyanka N, Dr.Pushpa RaviKumar,” Usage of Data mining techniques in predicting thediseases – Naïve Bayes & Decision tree”, International Conference on circuits Power and Computing Technologies [ICCPCT]

11. Purushottam, Prof. (Dr.) Kanak Saxena, Richa Sharma,” Efficient Heart Disease Prediction



A Empirical study using Data Mining

Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018)

Part Number: 5386-1974-2.

EM, Famina KP, Fida K, Aswathi P, Shanid M, Hena M, “Heart Disease

Technique”, International Conference on Electronics, Communication and Aerospace Technology

Salma Banu N.K, Suma Swamy, “Prediction of ease at early stage using Data Mining

and Big Data Analytics: A Survey”, International Conference on Electrical, Electronics, Communication, Computer and Optimization

Ankita Dewan, Meghna Sharma, “Prediction of Technique in Data

2nd International Conference on Computing for Sustainable Global

Monika Gandhi, Dr. Shailendra Narayan Singh, “Predictions in Heart Disease Using Techniques

ta Mining”,1st International Conference on Futuristic trend in Computational Analysis and

(ABLAZE-2015).

JABBAR, B.L Deekshatulu, Priti Chndra, Alternating decision trees for early diagnosis of

heart disease”, Proceedings of International Conference on Circuits, Communication, Control

Jabbar, Shirina samreen, “Heart disease prediction system based on hidden naïve bayes

Volume 13 Issue 3, Ver 1.0,

Implementation Identification of Drug Tree ID3 Algorithm”,

Priyanka N, Dr.Pushpa RaviKumar,” Usage of Data mining techniques in predicting the Heart

ïve Bayes & Decision tree”, International Conference on circuits Power and Computing Technologies [ICCPCT], 2017.

Purushottam, Prof. (Dr.) Kanak Saxena, Richa Sharma,” Efficient Heart Disease Prediction

System using Decision Tree”, International Conference on Computing, Communication and Automation (ICCCA2015).

12. Rashmi G Saboji, Prem Kumar Ramesh,”Scalable Solution for Heart Disease Predictionusing Classification Mining Technique”, International Conference on Energy, Communication, Data AnComputing (ICECDS-2017).

13. Mai Shouman, Tim Turner, Rob Stocker, “USING DATA MINING TECHNIQUES IN HEARTDISEASE DIAGNOSIS AND TREATMENT”, Japan-Egypt Conference on Electronics, Communications and Computers,2012.

14. C. Sowmiya, Dr. P. Sumitra, “Heart Disease DiagnosisTechniques”, IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNIQUES IN CONTROL, OPTIMIZATION AND SIGNAL PROCESSING, 2017.

15. P. Sudeshna, S. Bhanumathi,Hamlin,” Identifying Symptoms and Treatment for Heart Disease from Biomedical Literature Using Text Data Mining”,Conference on Computation of Power,Information and Communication (ICCPEIC), 2017.

16. Theresa Princy. R, J. Thomas, “Human Heart Disease Prediction System using DataTechniques”, International Conference on Circuit, Power and Computing Technologies [ICCPCT], 2016.

17. Yong Wang, Ya-Wei Zhao,”Data Mining Algorithms to Cloud Computing Platform when Dealing Big Data”, Conference on CyberComputing and Knowledge Discovery, 2014.

18. Huihui Zhao, Jianxin Chen, Qin Hou, “Characteristic Pattern Study of Coronary heart disease with Qi-yin deficiencyDecision Tree”, Second International Conference on Intelligent Computation Technology and Automation, 2009.

19. Huihui Zhao , Shuwen Guo, Jianxin Chen , Qi Shi,Juan Wang, Chenglong Zheng, Peng Tan,” Characteristic Pattern Study of Coronary Heart Disease with Blood Stasis SyndrDecision Tree”, of biological systems,Dec. 2013, pp. 435-451.


Dec 2018 Page: 1173

Decision Tree”, International Conference on Computing, Communication and Automation (ICCCA2015).

Rashmi G Saboji, Prem Kumar Ramesh,” A Scalable Solution for Heart Disease Prediction using Classification Mining Technique”, International Conference on Energy, Communication, Data Analytics and Soft

2017).

Mai Shouman, Tim Turner, Rob Stocker, “USING DATA MINING TECHNIQUES IN HEART DISEASE DIAGNOSIS AND TREATMENT”,

Egypt Conference on Electronics, Communications and Computers,2012.

itra, “Analytical Study of Heart Disease Diagnosis Using Classification

IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNIQUES IN CONTROL, OPTIMIZATION AND SIGNAL PROCESSING, 2017.

Bhanumathi, M. R, Anish Symptoms and Treatment

from Biomedical Literature Data Mining”, International

Conference on Computation of Power, Energy, Information and Communication (ICCPEIC),

Theresa Princy. R, J. Thomas, “Human Heart iction System using Data Mining International Conference on Circuit,

Power and Computing Technologies [ICCPCT],

Wei Zhao,” Transplantation of Data Mining Algorithms to Cloud Computing

Dealing Big Data”, International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2014.

Huihui Zhao, Jianxin Chen, Qin Hou, Characteristic Pattern Study of Coronary heart

yin deficiency syndrome based on International Conference

on Intelligent Computation Technology and

Huihui Zhao , Shuwen Guo, Jianxin Chen , Qi Shi, Juan Wang, Chenglong Zheng, Peng Tan,” Characteristic Pattern Study of Coronary Heart Disease with Blood Stasis Syndrome Based on

of biological systems, vol. 15,

Analytical Study of Diagnosis for Angioplasty and Stents Patients using Improved Classification...

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