We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Diagnosis of Cardiovascular Diseases using Artificial Intelligence Techniques: A Review

by Tazeen Tasneem, Mir Md. Jahangir Kabir, Shuxiang Xu, Tabeen Tasneem
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 3
Year of Publication: 2021
Authors: Tazeen Tasneem, Mir Md. Jahangir Kabir, Shuxiang Xu, Tabeen Tasneem
10.5120/ijca2021921313

Tazeen Tasneem, Mir Md. Jahangir Kabir, Shuxiang Xu, Tabeen Tasneem . Diagnosis of Cardiovascular Diseases using Artificial Intelligence Techniques: A Review. International Journal of Computer Applications. 183, 3 ( May 2021), 1-25. DOI=10.5120/ijca2021921313

@article{ 10.5120/ijca2021921313,
author = { Tazeen Tasneem, Mir Md. Jahangir Kabir, Shuxiang Xu, Tabeen Tasneem },
title = { Diagnosis of Cardiovascular Diseases using Artificial Intelligence Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { May 2021 },
volume = { 183 },
number = { 3 },
month = { May },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number3/31905-2021921313/ },
doi = { 10.5120/ijca2021921313 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:43.995295+05:30
%A Tazeen Tasneem
%A Mir Md. Jahangir Kabir
%A Shuxiang Xu
%A Tabeen Tasneem
%T Diagnosis of Cardiovascular Diseases using Artificial Intelligence Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 3
%P 1-25
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the last couple of decades, many techniques have been introduced for medical support system. One alarming field in medical health care is cardiovascular disease as millions of deaths occur every year because of this. Thus, diagnosis of heart disease has always been one of the most important issues. For predicting and diagnosis of cardiovascular disease, skilled and experienced physicians are needed. As this is an era of technology, researchers have been proposed many algorithms and learning techniques for assisting the physicians. The aim of this research work is to thoroughly analyze these algorithms and methods. This article has explored the used datasets, feature selection techniques and missing value imputation methods, and finally compared their performances.

References
  1. Resul Das, Ibrahim Turkoglu, and Abdulkadir Sengur. Effective diagnosis of heart disease through neural networks ensembles. Expert Syst. Appl., 36:7675–7680, 05 2009.
  2. WHO. Cardiovascular diseases (cvds). https://www.who. int/, last accessed on 03-04-2020.
  3. WHO. Noncommunicable diseases (ncd) country profiles. 2018.
  4. Yaowapa Maneerat, K. Prasongsukarn, Surachet Benjathummarak, and W. Dechkhajorn. Intersected genes in hyperlipidemia and coronary bypass patients: Feasible biomarkers for coronary heart disease. Atherosclerosis, 252:e183–e184, 09 2016.
  5. Takahiro Nakashima, Teruo Noguchi, Seiichi Haruta, Yusuke Yamamoto, Shuichi Oshima, Koichi Nakao, Yasuyo Taniguchi, Junichi Yamaguchi, Kazufumi Tsuchihashi, Atsushi Seki, Tomohiro Kawasaki, Tatsuro Uchida, Nobuhiro Omura, Migaku Kikuchi, Kazuo Kimura, Hisao Ogawa, Shunichi Miyazaki, and Satoshi Yasuda. Prognostic impact of spontaneous coronary artery dissection in young female patients with acute myocardial infarction: A report from the angina pectoris-myocardial infarction multicenter investiga
  6. James Zebrack, Jeffrey Anderson, Chloe Maycock, Benjamin Horne, Tami Bair, and Joseph Muhlestein. Usefulness of high-sensitivity c-reactive protein in predicting long-term risk of death or acute myocardial infarction in patients with unstable or stable angina pectoris or acute myocardial infarction. The American journal of cardiology, 89:145–9, 01 2002.
  7. Barry Robson and Srinidhi Boray. Implementation of a web based universal exchange and inference language for medicine: Sparse data, probabilities and inference in data mining of clinical data repositories. Computers in Biology and Medicine, 2193:82–102, 09 2015.
  8. Seyed Shenas, Bijan Raahemi, Mohammad Tekieh, and Craig Kuziemsky. Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes. Computers in Biology and Medicine, 53, 10 2014.
  9. Jae-Kwon Kim, Jong-Sik Lee, Dong-Kyun Park, Yong-Soo Lim, Youngho Lee, and Eun-Young Jung. Adaptive mining prediction model for content recommendation to coronary heart disease patients. Cluster Computing, 17, 09 2013.
  10. U Rajendra Acharya, Oliver Faust, Nahrizul Adib Kadri, Jasjit Suri, andWenwei Yu. Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in Biology and Medicine, 43:1523–9, 10 2013.
  11. Carlo Barbieri, Flavio Mari, Andrea Stopper, Emanuele Gatti, Pablo Escandell-Montero, Jos Martnez-Martnez, and Jos Martn-Guerrero. A new machine learning approach for predicting the response to anemia treatment in a large cohort of end stage renal disease patients undergoing dialysis. Computers in Biology and Medicine, 61, 03 2015.
  12. Dr. Subhash Pandey. Data mining techniques for medical data: A review. 11 2016.
  13. WHO. Cardiovascular disease. https://www.who.int/ cardiovascular_diseases/about_cvd/en/.
  14. Heart disease and stroke statistics. 2019.
  15. Stanley Davidson, Stuart Ralston, Ian Penman, Mark Strachan, and Richard Hobson. Davidson’s principles and practice of medicine a textbook for students and doctors. Elsevier, 23 edition, 2018.
  16. World Heart Federation. Rheumatic heart disease: A preventable, treatable form of cardiovascular disease. https://www.world-heart-federation. org/programmes/rheumatic-heart-disease/, last accessed on 10-04-2020.
  17. Prevention of cardiovascular disease. 2007.
  18. Lawrence D. Chilnick. Heart disease: An essential guide for the newly diagnosed. 2008.
  19. Chang-Sik Son, Yoon-Nyun Kim, Hyung-Seop Kim, Hyoung-Seob Park, and Min-Soo Kim. Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. J. of Biomedical Informatics, 45(5):9991008, October 2012.
  20. Resul Das and Abdulkadir Sengur. Evaluation of ensemble methods for diagnosis of valvular heart disease. Expert Systems with Applications, 37:5110–5115, 07 2010.
  21. Hongzeng Xu, Zhiying Duan, Chi Miao, Song Geng, and Yuanzhe Jin. Development of a diagnosis model for coronary artery disease. Indian heart journal, 69(5):634 – 639, 2017.
  22. Ali Muhammad Usman, Umi Kalsom Yusof, and Syibrah Naim. Cuckoo inspired algorithms for feature selection in heart disease prediction. International Journal of Advances in Intelligent Informatics, 4:95–106, 2018.
  23. Kalia Orphanou, Arianna Dagliati, Lucia Sacchi, Athena Stassopoulou, Elpida Keravnou, and Riccardo Bellazzi. Incorporating repeating temporal association rules in nave bayes classifiers for coronary heart disease diagnosis. Journal of Biomedical Informatics, 81, 03 2018.
  24. Long Nguyen Cong, Phayung Meesad, and Herwig Unger. A highly accurate firefly based algorithm for heart disease prediction. Expert Systems with Applications, 06 2015.
  25. C. Beulah Christalin Latha and S. Carolin Jeeva. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16:100203, 2019.
  26. Chih-Wen Chen, Yi-Hong Tsai, Fang-Rong Chang, andWei- Chao Lin. Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results. Expert Systems, n/a(n/a):e12553.
  27. Emre Comak, Ahmet Arslan, and Ibrahim Turkoglu. A decision support system based on support vector machines for diagnosis of the heart valve diseases. Computers in biology and medicine, 37:21–7, 02 2007.
  28. Muthukaruppan M S and M.J. Er. A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Systems with Applications, 39:1165711665, 10 2012.
  29. Himansu Das, Bighnaraj Naik, and H.S. Behera. Medical disease analysis using neuro-fuzzy with feature extraction model for classification. Informatics in Medicine Unlocked, 18:100288, 2020.
  30. Mohammad Shafenoor Amin, Yin Kia Chiam, and Kasturi Dewi Varathan. Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36:82 – 93, 2019.
  31. Kemal Polat, Salih Gne, and Slayman Tosun. Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing. Pattern Recognition, 39:2186–2193, 11 2006.
  32. Jesmin Nahar, Tasadduq Imam, Kevin Tickle, and Yi- Ping Phoebe Chen. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40:10861093, 03 2013.
  33. Anna Karen Grate-Escamila, Amir Hajjam El Hassani, and Emmanuel Andrs. Classification models for heart disease prediction using feature selection and pca. Informatics in Medicine Unlocked, 19:100330, 2020.
  34. Kindie Biredagn Nahato, Khanna Nehemiah Harichandran, and Kannan Arputharaj. Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Computational and Mathematical Methods in Medicine, 2015, 2015.
  35. Hui Chen, Chao Tan, Zan Lin, Tong Wu, and Yuanbo Diao. A feasibility study of diagnosing cardiovascular diseases based on blood/urine element analysis and consensus models. Comp. in Bio. and Med., 43(7):865–869, 2013.
  36. Abdulkadir Sengur. An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Syst. Appl., 35:214–222, 07 2008.
  37. V. R. Elgin Christo, H. Khanna Nehemiah, J. Brighty, and Arputharaj Kannan. Feature selection and instance selection from clinical datasets using co-operative co-evolution and classification using random forest. IETE Journal of Research, 0(0):1–14, 2020.
  38. Moloud Abdar, U. Rajendra Acharya, Nizal Sarrafzadegan, and Vladimir Makarenkov. Ne-nu-svc: A new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access, 7:167605–167620, 2019.
  39. T. Vivekanandan and N Ch Sriman Narayana Iyenger. Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Computers in Biology and Medicine, 90, 09 2017.
  40. Abdulkadir Sengur and Ibrahim Turkoglu. A hybrid method based on artificial immune system and fuzzy k-nn algorithm for diagnosis of heart valve diseases. Expert Systems with Applications, 35:1011–1020, 10 2008.
  41. Youness Khourdifi and Mohamed Bahaj. Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. International Journal of Intelligent Engineering and Systems, 12:242–252, 2019.
  42. Syed Muhammad Saqlain Shah, Faiz Ali Shah, Syed Adnan Hussain, and Safeera Batool. Support vector machinesbased heart disease diagnosis using feature subset, wrapping selection and extraction methods. Computers and Electrical Engineering, 84:106628, 2020.
  43. T. Vivekanandan and Swathi Narayanan. A hybrid risk assessment model for cardiovascular disease using cox regression analysis and a 2-means clustering algorithm. Computers in Biology and Medicine, 113:103400, 08 2019.
  44. Ibrahim Turkoglu, Ahmet Arslan, and Erdogan Ilkay. An expert system for diagnosis of the heart valve diseases. Expert Syst. Appl., 23:229–236, 10 2002.
  45. Seral zen and Salih Gne. Effect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizer. Digital Signal Processing, 18(4):635 – 645, 2008.
  46. Younas Khan, Usman Qamar, Muhammad Asad, and Babar Zeb. Applying feature selection and weight optimization techniques to enhance artificial neural network for heart disease diagnosis. In Intelligent Systems and Applications, pages 340–351. Springer International Publishing, 2020.
  47. P.K. Anooj. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University - Computer and Information Sciences, 24(1):27 – 40, 2012.
  48. Hongmei Yan, Yingtao Jiang, Jun Zheng, Chenglin Peng, and Qinghui Li. A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications, 30:272–281, 02 2006.
  49. Syed Muhammad Saqlain Shah, Muhammad Sher, Faiz Ali Shah, Imran Khan, Muhammad Usman Ashraf, Muhammad Awais, and Anwer Ghani. Fisher score and matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowledge and Information Systems, 58:139–167, 2018.
  50. Bayu Adhi Tama, Sun Im, and Seungchul Lee. Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. BioMed Research International, 2020, 2020.
  51. Imran Omurlu, Mevlut Ture, and A. Kurum. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Systems with Applications, 34:366–374, 01 2008.
  52. Kemal Polat, Seral zen, and Salih Gne. Automatic detection of heart disease using an artificial immune recognition system (airs) with fuzzy resource allocation mechanism and knn (nearest neighbour) based weighting preprocessing. Expert Systems with Applications, 32:625–632, 02 2007.
  53. N. Ghadiri Hedeshi and Mohammad Saniee Abadeh. Coronary artery disease detection using a fuzzy-boosting PSO approach. Comput. Intell. Neurosci., 2014:783734:1– 783734:12, 2014.
  54. M. Sudha. Evolutionary and neural computing based decision support system for disease diagnosis from clinical data sets in medical practice. Journal of Medical Systems, 41, 11 2017.
  55. Oluwarotimi Sauel, Mojisola Asogbon, Arun Sangaiah, Fang Peng, and Li Guanglin. An integrated decision support system based on ann and fuzzy ahp for heart failure risk prediction. Expert Systems with Applications, 68:163172, 10 2016.
  56. A.V. Kumar. Diagnosis of heart disease using fuzzy resolution mechanism. Journal of Artificial Intelligence, 5:47–55, 01 2012.
  57. T. V. Gestel, J. A. K. Suykens, G. Lanckriet, A. Lambrechts, B. D. Moor, and J. Vandewalle. Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel fisher discriminant analysis. Neural Computation, 14(5):1115–1147, 2002.
  58. Musa Peker. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and svm. 40(5), 05 2016.
  59. Smail Babaolu, Oguz Findik, and Erkan lker. A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst. Appl., 37:3177–3183, 04 2010.
  60. Roohallah Alizadehsani, Jafar Habibi, Mohammad Javad Hosseini, Hoda Mashayekhi, Reihane Boghrati, Asma Ghandeharioun, Behdad Bahadorian, and Zahra Sani. A data mining approach for diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 111, 03 2013.
  61. Gokulnath Chandra Babu and Shantharajah S. Periyasamy. An optimized feature selection based on genetic approach and support vector machine for heart disease. Cluster Computing, 22:14777–14787, 2019.
  62. Der-Chiang Li, Chiao-Wen Liu, and Susan C. Hu. A fuzzybased data transformation for feature extraction to increase classification performance with small medical data sets. Artificial Intelligence in Medicine, 52(1):45 – 52, 2011.
  63. Booma devi Sekar and Mingchui Dong. Function formula oriented construction of bayesian inference nets for diagnosis of cardiovascular disease. BioMed research international, 2014:376378, 08 2014.
  64. Zeinab Arabasadi, Roohallah Alizadehsani, Mohamad Roshanzamir, Hossein Moosaei, and Ali Yarifard. Computer aided decision making for heart disease detection using hybrid neural network - genetic algorithm. Computer Methods and Programs in Biomedicine, 141, 01 2017.
  65. Guang-Bin Huang, Xiaojian Ding, and Hongming Zhou. Optimization method based extreme learning machine for classification. Neurocomputing, 74(1):155 – 163, 2010. Artificial Brains.
  66. Swati Shilaskar and Ashok Ghatol. Feature selection for medical diagnosis : Evaluation for cardiovascular diseases. Expert Systems with Applications, 40(10):4146 – 4153, 2013.
  67. Jamal Alneamy and Rahma Alnaish. Heart disease diagnosis utilizing hybrid fuzzy wavelet neural network and teaching learning based optimization algorithm. Advances in Artificial Neural Systems, 2014, 09 2014.
  68. Ebenezer Olaniyi, Oyebade Oyedotun, and Adnan Khashman. Heart diseases diagnosis using neural networks arbitration. International Journal of Intelligent Systems and Applications, 7:75–82, 11 2015.
  69. H. Hannah Inbarani, Ahmad Taher Azar, and G. Jothi. Supervised hybrid feature selection based on pso and rough sets for medical diagnosis. Computer Methods and Programs in Biomedicine, 113(1):175 – 185, 2014.
  70. Yuehjen E. Shao, Chia-Ding Hou, and Chih-Chou Chiu. Hybrid intelligent modeling schemes for heart disease classification. Applied Soft Computing, 14:47 – 52, 2014.
  71. Amin Haq, Jian Li, Muhammad Memon, Shah Nazir, and Ruinan Sun. A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems, 2018:1–21, 12 2018.
  72. Yuh jye Lee and O. L. Mangasarian. Ssvm: A smooth support vector machine for classification. Computational Optimization and Applications, pages 5–22, 2001.
  73. Mehrbakhsh Nilashi, Othman bin Ibrahim, Hossein Ahmadi, and Leila Shahmoradi. An analytical method for diseases prediction using machine learning techniques. Computers and Chemical Engineering, 106:212 – 223, 2017.
  74. Xiao Liu, Xiaoli Wang, Qiang Su, Mo Zhang, Yanhong Zhu, and Qian Wang, Qiugen Wang. A hybrid classification system for heart disease diagnosis based on the rfrs method. Computational and Mathematical Methods in Medicine, 2017, 01 2017.
  75. Seral zen and Salih Gne. Attribute weighting via genetic algorithms for attribute weighted artificial immune system (awais) and its application to heart disease and liver disorders problems. Expert Syst. Appl., 36:386–392, 01 2009.
  76. Jae Kwon Kim and Sanggil Kang. Neural network-based coronary heart disease risk prediction using feature correlation analysis. Journal of Healthcare Engineering, 2017, 2017.
  77. Jamal Salahaldeen Majeed Alneamy, Zakaria A. Hameed Alnaish, S.Z. Mohd Hashim, and Rahma A. Hamed Alnaish. Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis. Computers in Biology and Medicine, 112:103348, 2019.
  78. Humar Kahramanli and Novruz Allahverdi. Design of a hybrid system for the diabetes and heart diseases. Expert Syst. Appl., 35:82–89, 07 2008.
  79. Thanh Nguyen, Abbas Khosravi, Douglas Creighton, and Saeid Nahavandi. Classification of healthcare data using genetic fuzzy logic system and wavelets. Expert Systems with Applications, 42, 10 2014.
  80. Liaqat Ali, Shafqat Ullah Khan, Noorbakhsh Amiri Golilarz, Imrana Yakubu, Iqbal Qasim, Adeeb Noor, and Redhwan Nour. A feature-driven decision support system for heart failure prediction based on x2 statistical model and gaussian naive bayes. Computational and Mathematical Methods in Medicine, 2019, 2019.
  81. Tien-Loc Le, Tuan-Tu Huynh, Lo-Yi Lin, Chih-Min Lin, and Fei Chao. A k-means interval type-2 fuzzy neural network for medical diagnosis. International Journal of Fuzzy Systems, 21:22582269, 2019.
  82. Moloud Abdar,Wojciech Ksiazek, U Rajendra Acharya, Ru- San Tan, Vladimir Makarenkov, and Pawel Plawiak. A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179:104992, 2019.
  83. Thanh Nguyen, Abbas Khosravi, Douglas Creighton, and Saeid Nahavandi. Medical data classification using interval type-2 fuzzy logic system and wavelets. Applied Soft Computing, 30, 05 2015.
  84. Aniruddha Dutta, Tamal Batabyal, Meheli Basu, and Scott T. Acton. An efficient convolutional neural network for coronary heart disease prediction. Expert Systems with Applications, 159:113408, 2020.
  85. Anam Mustaqeem, Syed Muhammad Anwar, Abdul Rashid Khan, and Muhammad Majid. A statistical analysis based recommender model for heart disease patients. International Journal of Medical Informatics, 108:134 – 145, 2017.
  86. Engin Avci. A new intelligent diagnosis system for the heart valve diseases by using genetic-svm classifier. Expert Syst. Appl., 36:10618–10626, 09 2009.
  87. Zahra Beheshti, Siti Mariyam Shamsuddin, Ebrahim Beheshti, and Siti Yuhaniz. Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Computing, 18, 11 2013.
  88. Silverstein, B. Silverstein Alvin, Silverstein Nunn Virginia, and Laura. Heart disease. 2006.
  89. NCBI. Ethnic differences in cardiovascular disease. https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC1767706/, last accessed on 10-04-2020.
  90. Uci machine learning repository. http://archive.ics. uci.edu/ml/datasets.php.
  91. Mohammad Ashraf Ottom, Girija Chetty, Dat Tran, and Dharmendra Sharma. Hybrid approach for diagnosing thyroid, hepatitis, and breast cancer based on correlation based feature selection and nave bayes. volume 7666, pages 272– 280, 11 2012.
  92. Dharmendra Modha and W. Spangler. Feature weighting in k-means clustering. Machine Learning, 52:217–237, 09 2003.
  93. TaoWang, Zhenxing Qin, Zhi Jin, and Shichao Zhang. Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning. Journal of Systems and Software, 83:1137–1147, 07 2010.
  94. Roohallah Alizadehsani, Moloud Abdar, Mohamad Roshanzamir, Abbas Khosravi, Parham Kebria, Fahime Khozeimeh, Saeid Nahavandi, Nizal Sarrafzadegan, and U Rajendra Acharya. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Computers in Biology and Medicine, 111:103346, 07 2019.
  95. Jiri Kaiser. Dealing with missing values in data. Journal of Systems Integration, 5:42–51, 01 2014.
  96. RunminWei, JingyeWang, Mingming Su, Erik Jia, Shaoqiu Chen, Tian-Lu Chen, and Yan Ni. Missing value imputation approach for mass spectrometry-based metabolomics data. Scientific Reports, 8, 12 2018.
  97. Yuntian Chen, Haibin Chang, Jin Meng, and Dongxiao Zhang. Ensemble neural networks (enn): A gradient-free stochastic method. Neural Networks, 110:170 – 185, 2019.
  98. Xindong Wu, Vipin Kumar, Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, G. Mclachlan, Shu Kay Angus Ng, Bing Liu, Philip Yu, Zhi-Hua Zhou, Michael Steinbach, David Hand, and Dan Steinberg. Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 12 2007.
  99. B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifiers. ACM press, pages 144–152, 1992.
  100. Maher Alaraj, Munir Majdalawieh, and Maysam F. Abbod. Improving binary classification using filtering based on k-nn proximity graphs. Journal of Big Data volume, 7, 03 2020.
  101. Wei Zhang and Feng Gao. An improvement to naive bayes for text classification. Procedia Engineering, 15:2160 – 2164, 2011. CEIS 2011.
  102. Monika Kabir, Mir Md. Jahangir Kabir, Shuxiang Xu, and Bodrunnessa Badhon. An empirical research on sentiment analysis using machine learning approaches. International Journal of Computers and Applications, 0(0):1–9, 2019.
  103. L.A. Zadeh. Fuzzy sets. Information and Control, 8(3):338 – 353, 1965.
Index Terms

Computer Science
Information Sciences

Keywords

Cardiovascular disease Feature selection Missing value imputation Artificial Neural Network Classification

Powered by PhDFocusTM