A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases
Tóm tắt
Từ khóa
Tài liệu tham khảo
Pu LN, Zhao Z, Zhang YT (2012) Investigation on heart risk prediction using genetic information. IEEE Trans Inf Technol Biomed 16(5):795–808
Namasudra S (2017) An improved attribute-based encryption technique towards the data security in cloud computing. Concurr Comput Pract Exerc 1:5. https://doi.org/10.1002/cpe.4364
Namasudra S, Roy P, Balamurugan B (2017) Cloud computing: fundamentals and research issues. In: Proceedings of the 2nd international conference on recent trends and challenges in computational models, IEEE, Tindivanam, India
Namasudra S, Nath S, Majumder A (2014) Profile based access control model in cloud computing environment. In: Proceedings of the international conference on green computing, communication and electrical engineering, IEEE, Coimbatore, India, pp. 1–5
Namasudra S, Roy P (2017) A new secure authentication scheme for cloud computing environment. Concurr Comput Pract Exerc 29(20):e3864. https://doi.org/10.1002/cpe.3864
Namasudra S, Roy P (2016) Secure and efficient data access control in cloud computing environment: a survey. Multiagent Grid Syst Int J 12(2):69–90
Namasudra S, Roy P (2017) Time saving protocol for data accessing in cloud computing. IET Commun 11(10):1558–1565
Namasudra S, Roy P, Balamurugan B, Vijayakumar P (2017) Data accessing based on the popularity value for cloud computing. In: Proceedings of the international conference on innovations in information, embedded and communications systems (ICIIECS), IEEE, Coimbatore, India
Namasudra S, Roy P (2015) Size based access control model in cloud computing. In: Proceedings of the international conference on electrical, electronics, signals, communication and optimization, IEEE, Visakhapatnam, India, pp. 1–4
Namasudra S, Roy P (2017) A new table based protocol for data accessing in cloud computing. J Inf Sci Eng 33(3):585–609
Namasudra S, Roy P (2018) PpBAC: popularity based access control model for cloud computing. J Org End User Comput 30(4):14–31
Sarkar S, Saha K, Namasudra S, Roy P (2015) An efficient and time saving web service based android application. SSRG Int J Comput Sci Eng 2(8):18–21
Namasudra S (2018) Cloud computing: a new era. J Fundam Appl Sci 10(2):113–135
Li M, Yu S, Ren K, Lou W (2010) Securing personal health records in cloud computing: patient-centric and fine-grained data access control in multi-owner settings. In: Proceedings of the international conference on security and privacy in communication systems, pp. 89–106
Liu X, Lu R, Ma J, Chen L, Qin B (2016) Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J Biomed Health Inform 20(2):655–668
Mathew G, Obradovic Z (2011) A privacy-preserving framework for distributed clinical decision support. In: Proceedings of the computational advances in bio and medical sciences, pp. 129–134
Polat K, Güneş S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Proc 17(4):694–701
Sánchez AS, Iglesias-Rodríguez FJ, Fernández PR, Juez FJDC (2016) Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders. Int J Ind Ergon 52:92–99
Huang F, Wang S, Chan CC (2012) Predicting disease by using data mining based on healthcare information system. In: Proceedings of the IEEE international conference on granular computing, pp. 191–194
Krishnaiah V, Narsimha DG, Chandra NS (2013) Diagnosis of lung cancer prediction system using data mining classification techniques. Int J Comput Sci Inf Technol 4(1):39–45
Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675–7680
Bashir S, Qamar U, Khan FH (2015) BagMOOV: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas Phys Eng Sci Med 38(2):305–323
Shilaskar S, Ghatol A (2013) Feature selection for medical diagnosis: evaluation for cardiovascular diseases. Expert Syst Appl 40(10):4146–4153
Shao YE, Hou CD, Chiu CC (2014) Hybrid intelligent modeling schemes for heart disease classification. Appl Soft Comput 14(5):47–52
Guan W, Gray A, Leyffer S (2009) Mixed-integer support vector machine. In: Proceedings of the NIPS workshop on optimization for machine learning, pp. 1–6
Hoa NS (1996) Some efficient algorithms for rough set methods. In: Proceedings IPMU’96 Granada, Spain, pp. 1541–1547
Ye D, Chen Z, Ma S (2013) A novel and better fitness evaluation for rough set based minimum attribute reduction problem. Inf Sci 222:413–423
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Proceedings of the 5th international conference on stochastic algorithms: foundations and applications. Springer, Berlin, pp. 169–178
Tsumoto S (2000) Problems with mining medical data. In: Proceedings of the 24th annual international computer software and applications conference, IEEE, Taipei, Taiwan
Neagoe VE, Iatan IF, Grunwald S (2003) A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis. In: The proceedings of the AMIA Annual Symposium, pp. 494–498
Ordonez C. (2004). Improving heart disease prediction using constrained association rules. In: Seminar presentation at University of Tokyo
Noh K, Lee HG, Shon HS, Lee BJ, Ryu KH (2006) Associative classification approach for diagnosing cardiovascular disease (LNCIS, 345). Springer, Berlin, pp 721–727
Koutsojannis C, Hatzilygeroudis I (2007) Using a neurofuzzy approach in medical application (LNCS, 4693). Springer, Berlin, pp 477–484
Tsipouras MG, Exarchos TP, Fotiadis DI, Kotsiam AP, Vakalis KV, Naka KK, Michalis LK (2008) Automated diagnosis of coronary artery disease based on data mining and Fuzzy modeling. IEEE Trans Inf Technol Biomed 12(4):447–458
Vazirani H, Kala R, Shukla A, Tiwari R (2010) Use of modular neural network for heart disease. Int J Comput Commun Technol 1(2–4):88–93
Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J King Saud Univ Comput Inf Sci 24(1):27–40
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Bai Y, Han X, Chen T, Yu H (2015) Quadratic kernel-free least squares support vector machine for target diseases classification. J Comb Opt 30(4):850–870
Sharawardi NSA, Choo YH, Chong SH, Muda AK, Goh OS (2014) Single channel sEMG muscle fatigue prediction: an implementation using least square support vector machine. In: Proceedings of the 4th world congress on information and communication technologies, IEEE, Bandar Hilir, Malaysia, pp. 320–325
Han J, Kamber M, Pei J (2011) Data mining concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, Burlington
Singh YN, Gupta P (2007) Quantitative evaluation of normalization techniques of matching scores in multimodal biometric systems (LNCS, 4642). Springer, Berlin, pp 574–583
Brigham EO (1988) The fast Fourier transform and its applications. Prentice-Hall, Englewood Cliffs
Alfred M (1999) Signal analysis wavelets, filter banks, time-frequency transforms and applications. Wiley, New York
Li S, Tang B, He H (2016) An imbalanced learning based MDRTB early warning system. J Med Syst 40(7):1–9
Gao H, Jian S, Peng Y, Liu X (2016) A subspace ensemble framework for classification with high dimensional missing data. Multidimens Syst Signal Process 28(4):1309–1324
Lafta R, Zhang J, Tao X, Li Y, Tseng VS (2015) An intelligent recommender system based on short-term risk prediction for heart disease patients. In: Proceedings IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), IEEE, Singapore, Singapore, pp. 102–105
Lafta R, Zhang J, Tao X, Li Y, Tseng VS, Luo Y, Chen F (2016) An intelligent recommender system based on predictive analysis in telehealthcare environment. Web Intell 14(4):325–336
Rizwan P, Rajsekhara Babu M, Suresh K (2017) Design and development of low investment smart hospital using internet of things through innovative approaches. Biomed Res 28(11):4979–4985