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

1. International Journal of Trend in International Open Access Journal International Open Access Journal | www.ijtsrd.com International Journal of Trend in Scientific Research and Development (IJTSRD) Research and Development (IJTSRD) www.ijtsrd.com ISSN No: 2456 ISSN No: 2456 - 6470 | Volume - 3 | Issue – 1 | Nov 1 | Nov – Dec 2018 Analytical Study Stents Patients using Improved Classification Technique sing Improved Classification Technique sing Improved Classification Technique Analytical Study of Diagnosis for Angioplasty Angioplasty and Faisal Riyaz Wani1, Er. Harwinder Kaur2 Faisal Riyaz Wani 1M.Tech M.Tech CSE, 2Assistant Professor Engineering, St. Soldier Institute of Engineering Near NIT, Jalandhar, Punjab, India Department of Computer Sc. & Engineering & Technology, 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 de 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 fro 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 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), Artif 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 provi and 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 TECHNIQUES IN HEART DISEASE Although applying data mining is beneficial to healthcare, disease diagnosis, and treatment, few researches have investigated producing treatment 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 Ali generating treatment plans for acute upper infection disease patients using a decision tree. The model 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 mining naïve bayes, neural network, and genetic still needs investigation. DATA MINING IN PREDICTION OF HEART DISEASE Despite the fact that data mining has more than one decade, its poten realized now. Data mining join factual examination, machine learning and database hidden patterns and connections from substantial databases Data mining methodologies: supervised and unsupervised learning’s. In learning, a training set is utilized to parameters though in unsupervised training set is utilized like in K two 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 classification models while Regression, Association 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 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 mainly uses uses two 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 DATAMINING DATAMINING IN HEART DISEASE Although applying data mining is beneficial to , disease diagnosis, and treatment, few @ IJTSRD | Available Online @ www.ijtsrd.com www.ijtsrd.com | Volume – 3 | Issue – 1 | Nov-Dec 2018 Dec 2018 Page: 1170

2. International Journal of Trend in Scientific Research and Development International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 (IJTSRD) ISSN: 2456-6470 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-programmed improving 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 Algorithm The Improved Classification Algorithm as 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 r training data that represents the rest of data to measure the error of the tree for each node of this tree do Select randomly: m features to determine the decision at the node and calculate the best split accordingly. end for end for Rules and Clustering are examples of prediction algorithm. In this prediction of heart disease, 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 Forest for prediction of patients with heart disease. The classifiers 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 consistently 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. BJECTIVESOFTHEPROPOSEDST The 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. Rules and Clustering are examples of prediction 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 we will use the we will use the following classification models of data mining are analyze the computation parameters for improving efficiency of data set. To critically analyze the diagnosis for angioplasty and stents patients using a improved classification To enhance the prediction of classifiers, genetic integrated; the genetic search resulted in 13 contributes more towards the programmed algorithm for improving efficiency of decision tree technique. The classifiers research work focuses on above stated objectives im in increase the efficiency on correctly classified instances and reducing error rate. 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 Algorithm technique outperforms such as Naïve Bayes and incorporating feature subset construction time. classification and after reduction of attributes Improved Classification Algorithm is explained Proposed technique technique performs , number of features to M determine the number of features at a node of The proposed system will have the following features: Collection of health care data of heart disease. Storage and Processing of data using Weka 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 algorithm. Analyzing performance in terms of accuracy and features to determine the 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 node and calculate the best split accordingly. dataset. Results Fig 1: The bar graph shows all attributes : The bar graph shows all attributes @ IJTSRD | Available Online @ www.ijtsrd.com www.ijtsrd.com | Volume – 3 | Issue – 1 | Nov-Dec 2018 Dec 2018 Page: 1171

3. International Journal of Trend in Scientific Research and Development International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 (IJTSRD) ISSN: 2456-6470 Comparison of CCI, ICI, ER Comparison of CCI, ICI, ER 120 100 80 Values 60 40 20 0 OneR ZeroR NaiveBayes ICA CCI 80.28 56.77 83.12 88.08 88.08 ICI 19.71 43.22 11.87 11.91 11.91 ER 89.62 100 74.9 62.38 62.38 Fig 2 Shows the values of CCI, ICI and Error Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms CI and Error Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms CI and Error Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms Analysis of TP Rate 1.2 1 0.8 Units 0.6 0.4 0.2 0 ICA NaiveBayes ZeroR OneR OneR Positive 0.824 0.777 0.672 0 0 Negative 0.924 0.128 0.903 1 1 Fig 3: Shows the values of TP Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms Shows the values of TP Rate of OneR, ZeroR, Naïve Bayes, and ICA Algorithms 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 of disease is having some success, the use of data mining techniques 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 in the suitable treatment for heart disease patients needs further investigation. This research identifies gaps in the research on heart disease diagnosis and treatment and proposes a model to systematically close those to systematically close those gaps to discover if applying data heart disease treatment data can provide performance as that achieved in diagnosing heart disease 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 Evolutionary Rule Conference on "Computational Intelligence and Communication Technology" (CICT 2018). Communication Technology" (CICT 2018). wide increasing mortality of gaps to discover if applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart 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, Improved Classification Algorithm (ICA) shows the better results in correctly classified instances, incorrect classified instances, error rate, TP Rate, FP Measure Values. techniques has shown of heart disease, so data mining technique in selecting Aakash Chauhan, Aditya Jain, Purushottam Sharma, “Heart Disease the suitable treatment for heart disease patients needs Prediction Prediction using using identifies gaps in Learning”, Learning”, International on heart disease diagnosis and treatment Conference on "Computational Intelligence and @ IJTSRD | Available Online @ www.ijtsrd.com www.ijtsrd.com | Volume – 3 | Issue – 1 | Nov-Dec 2018 Dec 2018 Page: 1172

4. International Journal of Trend in Scientific Research and Development International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 (IJTSRD) ISSN: 2456-6470 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 Communication and ICECA 2017. 4.Salma Banu N.K, Suma Swamy, “ Heart Disease at early stage using and Big Data Analytics: A Survey”, Conference on Communication, Computer and Optimization Techniques (ICEECCOT), 2016. 5.Ankita Dewan, Meghna Sharma, “Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification”, Conference on Computing for Sustainable Global Development (INDIACom), 2015. 6.Monika Gandhi, Dr. Shailendra Narayan Singh, “Predictions in Heart Disease Using of 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 of heart disease”, Proceedings of Conference on Circuits, Communication, Control and Computing (I4C 2014). 8.M. A. Jabbar, Shirina samreen, “ prediction system based on hidden classifier”, GJCST, Volume 13 Issue 3 15-25,(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 the diseases – 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 Sharma,” Efficient Heart Disease Prediction A Empirical study using Data Mining System using Decision Tree”, International Conference on Computing, Communication and Automation (ICCCA2015). 12.Rashmi G Saboji, Prem Kumar Ramesh,” Scalable Solution for Heart Disease Prediction using Classification International Conference Communication, Data Computing (ICECDS-2017). 13.Mai Shouman, Tim Turner, Rob Stocker, “USING DATA MINING TECHNIQUES IN HEART DISEASE DIAGNOSIS AND TREATMENT”, Japan-Egypt Conference Communications and Computers,2012. 14.C. Sowmiya, Dr. P. Sumitra, “ Heart Disease Diagnosis Techniques”, IEEE CONFERENCE ON 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 Conference on Computation of Power, Information and Communication (ICCPEIC), 2017. 16.Theresa Princy. R, J. Thomas, “Human Heart Disease Prediction System using Data Techniques”, 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 Cyber Computing and Knowledge Discovery, 2014. 18.Huihui Zhao, Jianxin “Characteristic Pattern Study of Coronary heart disease with Qi-yin deficiency Decision 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 Syndr Decision Tree”, of biological systems, Dec. 2013, pp. 435-451. 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 International Conference Communication, Data 2017). Mai Shouman, Tim Turner, Rob Stocker, “USING DATA MINING TECHNIQUES IN HEART DISEASE DIAGNOSIS AND TREATMENT”, Egypt Conference Communications and Computers,2012. itra, “Analytical Study of Heart Disease Diagnosis Using Classification IEEE CONFERENCE ON TECHNIQUES IN CONTROL, OPTIMIZATION AND SIGNAL PROCESSING, 2017. Bhanumathi, M. R, Anish Symptoms and Treatment from Biomedical Literature Data Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018) Part Number: 5386-1974-2. Mining Mining Technique”, Technique”, Energy, Energy, and on on EM, Famina KP, Fida K, An Analytics Soft Aswathi P, Shanid M, Hena M, “Heart Disease Technique”, Electronics, International Communication Conference and on on Electronics, Technology Technology Aerospace Aerospace on on Electronics, Electronics, Salma Banu N.K, Suma Swamy, “Prediction of ease at early stage using Data Mining and Big Data Analytics: A Survey”, International Conference on Communication, Computer and Optimization Electrical, Electrical, Electronics, Electronics, INTERNATIONAL INTERNATIONAL INTELLIGENT INTELLIGENT Ankita Dewan, Meghna Sharma, “Prediction of Technique in Data 2nd 2nd International International Conference on Computing for Sustainable Global Mining”, Mining”, International Monika Gandhi, Dr. Shailendra Narayan Singh, “Predictions in Heart Disease Using Techniques ta Mining”,1st International Conference on Futuristic trend in Computational Analysis and Theresa Princy. R, J. Thomas, “Human Heart iction System using Data Mining International Conference on Circuit, Power and Computing Technologies [ICCPCT], (ABLAZE-2015). JABBAR, B.L Deekshatulu, Priti Chndra, Alternating decision trees for early diagnosis of International Conference on Circuits, Communication, Control Wei Zhao,” Transplantation of Data Mining Algorithms to Cloud Computing Dealing Big Data”, International Conference on Cyber-Enabled Computing and Knowledge Discovery, 2014. Huihui Zhao, Jianxin Characteristic Pattern Study of Coronary heart yin deficiency syndrome based on International Conference on Intelligent Computation Technology and Jabbar, Shirina samreen, “Heart disease prediction system based on hidden naïve bayes Volume 13 Issue 3, Ver 1.0, Distributed Chen, Chen, Qin Qin Hou, Hou, 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 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, @ IJTSRD | Available Online @ www.ijtsrd.com www.ijtsrd.com | Volume – 3 | Issue – 1 | Nov-Dec 2018 Dec 2018 Page: 1173