1,037
Views
52
CrossRef citations to date
0
Altmetric
Articles

Medical Internet of things using machine learning algorithms for lung cancer detection

& ORCID Icon
Pages 591-623 | Received 25 Feb 2020, Accepted 14 Aug 2020, Published online: 31 Aug 2020

References

  • Abadi, M. , Agarwal, A. , Barham, P. , Brevdo, E. , Chen, Z. , Citro, C. , …  Ghemawat, S. (2016). “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv:1603.04467.
  • Aceto, G. , Persico, V. , & Pescapé, A. (2020). Industry 4.0 and health: internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration , 18 , 100129.
  • Agrawal, A. , Misra, S. , Narayanan, R. , Polepeddi, L. , & Choudhary, A. (2011). A lung cancer outcome calculator using ensemble data mining on SEER data . Proceedings of the Tenth International Workshop on Data mining in Bioinformatics , ACM.
  • Agrawal, A. , Misra, S. , Narayanan, R. , Polepeddi, L. , & Choudhary, A. (2012). Lung cancer survival prediction using ensemble data mining on seer data. Scientific Programming , 20 (1), 29–42.
  • Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications , 36 (2), 3240–3247.
  • Akman, E. , Karaman, A. S. , & Kuzey, C. (2020). Visa trial of international trade: Evidence from support vector machines and neural networks. Journal of Management Analytics , 7 (2), 231–252.
  • Al-Anni, R. , Hou, J. , Abdu-aljabar, R. D. , & Xiang, Y. (2017). Prediction of NSCLC recurrence from microarray data with GEP. IET Systems Biology , 11 (3), 77–85.
  • Al-Bahrani, R. , Agrawal, A. , & Choudhary, A. (2013). Colon cancer survival prediction using ensemble data mining on SEER data. 2013 IEEE International Conference on Big Data , Silicon Valley, CA, pp. 9–16.
  • Al-Kadi, O. S. , & Watson, D. (2008). Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Transactions on Biomedical Engineering , 55 (7), 1822–1830.
  • Alahmari, S. S. , Cherezov, D. , Goldgof, D. B. , Hall, L. O. , Gillies, R. J. , & Schabath, M. B. (2018). Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening. IEEE Access , 6 , 77796–77806.
  • Alanni, R. , Hou, J. , Azzawi, H. , & Xiang, Y. (2019). Cancer adjuvant chemotherapy prediction model for non-small cell lung cancer. IET Systems Biology , 13 (3), 129–135.
  • Ali, L. , Rahman, A. , Khan, A. , Zhou, M. I. , Javeed, A. , & Khan, J. A. (2019). An automated diagnostic system for heart disease prediction based on χ 2 statistical model and optimally configured deep neural network. IEEE Access , 7 , 34938–34945.
  • ALzubi, J. A. , Bharathikannan, B. , Tanwar, S. , Manikandan, R. , Khanna, A. , & Thaventhiran, C. (2019). Boosted neural network ensemble classification for lung cancer disease diagnosis. Applied Soft Computing , 80 , 579–591.
  • Anand, A. , & Suganthan, P. N. (2009). Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. Journal of Theoretical Biology , 259 (3), 533–540.
  • Anooj, P. K. (2012). 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.
  • Arshadi, N. , & Jurisica, I. (2005). Data mining for case-based reasoning in high-dimensional biological domains. IEEE Transactions on Knowledge and Data Engineering , 17 (8), 1127–1137.
  • Arunkumar, C. , & Ramakrishnan, S. (2019). Prediction of cancer using customised fuzzy rough machine learning approaches. Healthcare Technology Letters , 6 (1), 13–18.
  • Åström, F. , & Koker, R. (2011). A parallel neural network approach to prediction of Parkinson’s disease. Expert Systems with Applications , 38 (10), 12470–12474.
  • Azzawi, H. , Hou, J. , Xiang, Y. , & Alanni, R. (2016). Lung cancer prediction from microarray data by gene expression programming. IET Systems Biology , 10 (5), 168–178.
  • Babu, G. S. , & Suresh, S. (2013). Parkinson’s disease prediction using gene expression – a projection based learning meta-cognitive neural classifier approach. Expert Systems with Applications , 40 (5), 1519–1529.
  • Barakat, N. , Bradley, A. P. , & Barakat, M. N. H. (2010). Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Transactions on Information Technology in Biomedicine , 14 (4), 1114–1120.
  • Barbieri, C. , Mari, F. , Stopper, A. , Gatti, E. , Escandell-Montero, P. , Martínez-Martínez, J. M. , & Martín-Guerrero, J. D. (2015). 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 , 56–61.
  • Capriotti, E. , & Altman, R. B. (2011). A new disease-specific machine learning approach for the prediction of cancer-causing missense variants. Genomics , 98 (4), 310–317.
  • Çarklı Yavuz, B. , Yurtay, N. , & Ozkan, O. (2018). Prediction of protein secondary structure With clonal selection algorithm and multilayer perceptron. IEEE Access , 6 , 45256–45261.
  • Chen, A. H. , & Lin, C.-H. (2011). A novel support vector sampling technique to improve classification accuracy and to identify key genes of leukaemia and prostate cancers. Expert Systems with Applications , 38 (4), 3209–3219.
  • Chen, D. , Xing, K. , Henson, D. , Sheng, L. , Schwartz, A. M. , & Cheng, X. (2009). Developing prognostic systems of cancer patients by ensemble clustering. Journal of Biomedicine and Biotechnology , 2009, 1–7.
  • Chen, H.-L. , Liu, D.-Y. , Yang, B. , Liu, J. , & Wang, G. (2011). A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Systems with Applications , 38 (9), 11796–11803.
  • Chen, H.-L. , Yang, B. , Liu, J. , & Liu, D.-Y. (2011). A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Systems with Applications , 38 (7), 9014–9022.
  • Chen, M. , Hao, Y. , Hwang, K. , Wang, L. , & Wang, L. (2017). Disease prediction by machine learning over Big data from healthcare communities. IEEE Access , 5 , 8869–8879.
  • Chen, T. , Li, M. , Li, Y. , Lin, M. , Wang, N. , Wang, M. , …  Zhang, Z. (2015). MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems.CoRR abs/1512.01274. Retrieved from http://arxiv
  • Chi-Hsien, K. , & Nagasawa, S. (2019). Applying machine learning to market analysis: Knowing your luxury consumer. Journal of Management Analytics , 6 (4), 404–419.
  • Choi, H. , Yeo, D. , Kwon, S. , & Kim, Y. (2011). Gene selection and prediction for cancer classification using support vector machines with a reject option. Computational Statistics & Data Analysis , 55 (5), 1897–1908.
  • Çınar, M. , Engin, M. , Engin, E. Z. , & Ziya Ateşçi, Y. (2009). Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Systems with Applications , 36 (3), 6357–6361.
  • Cirujeda, P. , Cid, Y. D. , Müller, H. , Rubin, D. , Aguilera, T. A. , Loo, B. W. , …  Depeursinge, A. (2016). A 3-D riesz-covariance texture model for prediction of nodule recurrence in lung CT. IEEE Transactions on Medical Imaging , 35 (12), 2620–2630.
  • Dai, W. , Brisimi, T. S. , Adams, W. G. , Mela, T. , Saligrama, V. , & Paschalidis, I. (2015). Prediction of hospitalization due to heart diseases by supervised learning methods. International Journal of Medical Informatics , 84 (3), 189–197.
  • Das, A. , Rad, P. , Choo, K. K. R. , Nouhi, B. , Lish, J. , & Martel, J. (2019). Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression. Future Generation Computer Systems , 93 , 486–498.
  • Davi, C. , Pastor, A. , Oliveira, T. , Neto, F. B. L. , Braga-Neto, U. , Bigham, A. W. , …  Acioli-Santos, B. (2019). Severe dengue prognosis using human genome data and machine learning. IEEE Transactions on Biomedical Engineering , 66 (10), 2861–2868.
  • Delen, D. (2009). Analysis of cancer data: A data mining approach. Expert Systems , 26 (1), 100–112.
  • Delen, D. , & Patil, N. (2006). Knowledge extraction from prostate cancer data. Proceedings of the 39th Annual Hawaii International Conference on, vol.5 .
  • Delen, D. , Walker, G. , & Kadam, A. (2005). Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine , 34 (2), 113–127.
  • Dimitoglou, G. , Adams, J. A. , & Jim, C. M. (2012). Comparison of the C4.5 and a naive Bayes classifier for the prediction of lung cancer survivability. Journal of Computing , 4 (8), 1–9.
  • Dinh, A. , Miertschin, S. , Young, A. , & Mohanty, S. D. (2019). A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Medical Informatics and Decision Making , 19 (211), 1–15.
  • Ed-daoudy, A. , & Maalmi, K. (2019). A new internet of things architecture for real-time prediction of various diseases using machine learning on big data environment. Journal of Big Data , 6 , 104.
  • Emaminejad, N. , Qian, W. , Guan, Y. , Tan, M. , Qiu, Y. , Liu, H. , & Zheng, B. (2016). Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Transactions on Biomedical Engineering , 63 (5), 1034–1043.
  • Engchuan, W. , & Chan, J. H. (2015). Pathway activity transformation for multi-class classification of lung cancer datasets. Neurocomputing , 165 , 81–89.
  • Fan, Y. J. , Yin, Y. H. , Xu, L. , Zeng, Y. , & Wu, F. (2014). Iot based smart rehabilitation system. IEEE Transactions on Industrial Informatics , 10 (2), 1568–1577.
  • Fitriyani, N. L. , Syafrudin, M. , Alfian, G. , & Rhee, J. (2019). Development of disease prediction model based on ensemble learning approach for diabetes and hypertension. IEEE Access , 7 , 144777–144789.
  • Fradkin, D. , Muchnik, I. , & Schneider, D. (2005). Machine learning methods in the analysis of lung cancer survival data. DIMACS Technical Report .
  • Guo, J. , He, H. , He, T. , Lausen, L. , Li, M. , & Lin, H. (2020). GluonCV and GluonNLP: Deep learning in computer vision and natural language processing. Journal of Machine Learning Research , 21 , 1–7.
  • Haq, A. U. , Li, J. P. , Memon, M. H. , khan, J. , Malik, A. , Ahmad, T. , …  Shahid, M. (2019). Feature selection based on L1-norm support vector machine and effective recognition system for Parkinson’s disease using voice recordings. IEEE Access , 7 , 37718–37734.
  • Hawkins, S. H. , Korecki, J. N. , Balagurunathan, Y. , Gu, Y. , Kumar, V. , Basu, S. , …  Gillies, R. J. (2014). Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access , 2 , 1418–1426.
  • Hoogendoorn, M. , Moons, L. M. G. , Numans, M. E. , & Sips, R.-J. (2014). Utilizing data mining for predictive modeling of colorectal cancer using electronic medical records. International Conference on brain Informatics and Health BIH 2014: Brain Informatics and Health (pp 132–141).
  • Huang, Z. W. , Mcwilliams, A. , Lui, H. , Mclean, D. , Lan, S. , & Zeng, H. S. (2003). Near-infrared Raman spectroscopy for optical diagnosis of lung cancer. International Journal of Cancer , 107 (6), 1047–1052.
  • Jakhar, K. , & Hooda, N. (December). Big data deep learning framework using Keras: A case study of Pneumonia prediction. 2018 4th International Conference on computing communication and automation (ICCCA) (pp. 1–5). IEEE.
  • Jemal, A. , Bray, F. , Center, M. M. , Ferlay, J. J. , Ward, E. , & Forman, D. (2011). Global cancer statistics. Cancer Journal for Clinicians , 61 (2), 69–90.
  • Jia, Y. , Shelhamer, E. , Donahue, J. , Karayev, S. , Long, J. , & Girshick, R. (2014). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia , (pp. 675–678).
  • Jordanski, M. , Radovic, M. , Milosevic, Z. , Filipovic, N. , & Obradovic, Z. (2018). Machine learning approach for predicting wall shear distribution for abdominal aortic aneurysm and carotid bifurcation models. IEEE Journal of Biomedical and Health Informatics , 22 (2), 537–544.
  • Kaya, Y. , & Uyar, M. (2013). A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease. Applied Soft Computing , 13 (8), 3429–3438.
  • Ketkar, N. (2017). Deep learning with python: A hands-on introduction . Berkeley, CA : Apress.
  • Khalid, Z. , & Sezerman, O. U. (2018). Prediction of HIV drug resistance by combining sequence and structural properties. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 15 (3), 966–973.
  • Kim, T.-W. , Koh, D.-H. , & Park, C.-Y. (2010). Decision tree of occupational lung cancer using classification and regression analysis. Safety and Health at Work , 1 (2), 140–148.
  • Kotsavasiloglou, C. , Kostikis, N. , Hristu-Varsakelis, D. , & Arnaoutoglou, M. (2017). Machine learning-based classification of simple drawing movements in Parkinson's disease. Biomedical Signal Processing and Control , 31 , 174–180.
  • Kumar, D. , Sankar, V. , Clausi, D. , Taylor, G. W. , & Wong, A. (2019). SISC: End-to-end interpretable discovery radiomics-driven lung cancer prediction via stacked interpretable sequencing cells. IEEE Access , 7 , 145444–145454.
  • Lai, D. , Zhang, Y. , Zhang, X. , Su, Y. , & Bin Heyat, M. B. (2019). An automated strategy for early risk identification of sudden cardiac death by using machine learning approach on measurable arrhythmic risk markers. IEEE Access , 7 , 94701–94716.
  • Lee, B. J. , & Kim, J. Y. (2016). Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE Journal of Biomedical and Health Informatics , 20 (1), 39–46.
  • Lee, B. J. , Ku, B. , Nam, J. , Pham, D. D. , & Kim, J. Y. (2014). Prediction of fasting plasma glucose status using anthropometric measures for diagnosing type 2 diabetes. IEEE Journal of Biomedical and Health Informatics , 18 (2), 555–561.
  • Lee, J. , Keam, B. , Jang, E. J. , Park, M. S. , Lee, J. Y. , Kim, D. B. , …  Kim, H.-L. (2011). Development of a predictive model for type 2 diabetes mellitus using genetic and clinical data. Osong Public Health and Research Perspectives , 2 (2), 75–82.
  • Li, L. , Liu, W. , Zhang, H. , Jiang, Y. , Hu, X. , & Liu, R. (2019). Down syndrome prediction using a cascaded machine learning framework designed for imbalanced and feature-correlated data. IEEE Access , 7 , 97582–97593.
  • Li, M. , Xiang, Z. , Lian, Z. , Xiao, L. , Zhang, J. , & Wei, Z. (2018). Prediction of lung motion from four-dimensional computer tomography (4DCT) images using Bayesian registration and trajectory modelling. IEEE Access , 6 , 2803–2811.
  • Li, S. , Xu, L. , & Zhao, S. (2018). 5G internet of things: A survey. Journal of Industrial Information Integration , 10 , 1–9.
  • Lu, Y. (2019). Artificial intelligence A survey on evolution models applications and future trends. Journal of Management Analytics , 6 (4), 404–419.
  • Luo, J. , Ding, P. , Liang, C. , Cao, B. , & Chen, X. (2017). Collective prediction of disease-associated miRNAs based on transduction learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 14 (6), 1468–1475.
  • Luo, Y. , Shan, D. M. , Ray, D. , Matuszak, M. , Jolly, S. , Lawrence, T. , …  Naqa, I. E. (2019). Development of a fully cross-validated Bayesian network approach for local control prediction in lung cancer. IEEE Transactions on Radiation and Plasma Medical Sciences , 3 (2), 232–241.
  • Lynch, C. M. , Abdollahi, B. , Fuqua, J. D. , de Carlo, A. R. , Bartholomai, J. A. , Balgemann, R. N. , …  Frieboes, H. B. (2017). Prediction of lung cancer patient survival via supervised machine learning classification techniques. International Journal of Medical Informatics , 108 , 1–8.
  • Lynch, C. M. , Berkel, V. H. V. , & Frieboes, H. B. (2017). Application of unsupervised analysis techniques to lung cancer patient data. PLoS One , 12 (9), 1–18.
  • Ma, L. , Wang, D. D. , Zou, B. , & Yan, H. (2017). An eigen-binding site based method for the analysis of anti-EGFR drug resistance in lung cancer treatment. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 14 (5), 1187–1194.
  • Majid, A. , Ali, S. , Iqbal, M. , & Kausar, N. (2014). Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines. Computer Methods and Programs in Biomedicine , 113 (3), 792–808.
  • Memarian, N. , Kim, S. , Dewar, S. , EngelJr, J. , & Staba, R. J. (2015). Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Computers in Biology and Medicine , 64 , 67–78.
  • Mohabatkar, H. , Beigi, M. M. , & Esmaeili, A. (2011). Prediction of GABAA receptor proteins using the concept of chou's pseudo-amino acid composition and support vector machine. Journal of Theoretical Biology , 281 (1), 18–23.
  • Mohan, S. , Thirumalai, C. , & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access , 7 , 81542–81554.
  • Mohebian, M. R. , Marateb, H. R. , Mansourian, M. , AngelMañanas, M. , & Mokarian, F. (2017). A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized ensemble learning. Computational and Structural Biotechnology Journal , 15 , 75–85.
  • Munsell, B. C. , Wee, C. Y. , Keller, S. S. , Weber, B. , Elger, C. , da Silva, L. A. T. , …  Bonilha, L. (2015). Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage , 118 , 219–230.
  • Nilashi, M. , binIbrahim, O. , Ahmadi, H. , & Shahmoradi, L. (2017). An analytical method for diseases prediction using machine learning techniques. Computers & Chemical Engineering , 106 , 212–223.
  • Okada, H. , Hontsu, S. , Miura, S. , Asakawa, I. , Tamamoto, T. , Katayama, E. , …  Hasegawa, M. (2012). Changes of tumor size and tumor contrast enhancement during radiotherapy for Non-small-cell lung cancer May Be suggestive of treatment response. Journal of Radiation Research , 53 (2), 326–332.
  • Oztekin, A. , Delen, D. , & (James)Kong, Z. (2009). Predicting the graft survival for heart–lung transplantation patients: An integrated data mining methodology. International Journal of Medical Informatics , 78 (12), e84–e96.
  • Park, S. , Lee, S. J. , Weiss, E. , & Motai, Y. (2016). Intra- and inter-fractional variation prediction of lung tumors using fuzzy deep learning. IEEE Journal of Translational Engineering in Health and Medicine , 4 , 1–12.
  • Pati, J. (2019). Gene expression analysis for early lung cancer prediction using machine learning techniques: An eco-genomics approach. IEEE Access , 7 , 4232–4238.
  • Perveen, S. , Shahbaz, M. , Keshavjee, K. , & Guergachi, A. (2019). Metabolic syndrome and development of diabetes mellitus: Predictive modeling based on machine learning techniques. IEEE Access , 7 , 1365–1375.
  • Petousis, P. , Han, S. X. , Aberle, D. , & Bui, A. A. T. (2016). Prediction of lung cancer incidence on the low-dose computed tomography arm of the national lung screening trial: A dynamic Bayesian network. Artificial Intelligence in Medicine , 72 , 42–55.
  • Petousis, P. , Winter, A. , Speier, W. , Aberle, D. R. , Hsu, W. , & Bui, A. A. T. (2019). Using sequential decision making to improve lung cancer screening performance. IEEE Access , 7 , 119403–119419.
  • Prince, J. , Andreotti, F. , & De Vos, M. (2019). Multi-Source ensemble learning for the remote prediction of Parkinson's disease in the presence of source-wise missing data. IEEE Transactions on Biomedical Engineering , 66 (5), 1402–1411.
  • Qi, J. , Yang, P. , Min, G. , Amft, O. , Dong, F. , & Xu, L. (2017). Advanced internet of things for personalised healthcare systems: A survey. Pervasive and Mobile Computing , 41 , 132–149.
  • Raweh, A. A. , Nassef, M. , & Badr, A. (2018). A hybridized feature selection and extraction approach for enhancing cancer prediction based on DNA methylation. IEEE Access , 6 , 15212–15223.
  • Sattlecker, M. , Baker, R. , Stone, N. , & Bessant, C. (2011). Support vector machine ensembles for breast cancer type prediction from mid-FTIR micro-calcification spectra. Chemometrics and Intelligent Laboratory Systems , 107 (2), 363–370.
  • Sedaghat, N. , Fathy, M. , Modarressi, M. H. , & Shojaie, A. (2018). Combining supervised and unsupervised learning for improved miRNA target prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 15 (5), 1594–1604.
  • Skoymind . (2017, April 18). Deeplearning4j deep learning framework. Retrieved from https://deeplearning4j.org
  • Subasi, A. (2012). Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines. Computers in Biology and Medicine , 42 (8), 806–815.
  • Tan, C. , Chen, H. , & Xia, C. (2009). Early prediction of lung cancer based on the combination of trace element analysis in urine and an adaboost algorithm. Journal of Pharmaceutical and Biomedical Analysis , 49 (3), 746–752.
  • Tang, L.-J. , Jiang, J.-H. , Wu, H.-L. , Shen, G.-L. , & Yu, R.-Q. (2009). Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data. Talanta , 79 (2), 260–267.
  • Team, T. T. D. , Al-Rfou, R. , Alain, G. , Almahairi, A. , Angermueller, C. , Bahdanau, D. , …  Belikov, A. (2016). Theano: A python framework for fast computation of mathematical expressions. arXiv:1605.02688.
  • Tokui, S. , Okuta, R. , Akiba, T. , Niitani, Y. , Ogawa, T. , Saito, S. , …  Vincent, H. Y. (2019). “Chainer: A deep learning framework for accelerating the research cycle” KDD 19, August 4–8, 2019, Anchorage, AK, USA.
  • Tong, D. L. , & Schierz, A. C. (2011). Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data. Artificial Intelligence in Medicine , 53 (1), 47–56.
  • Valdés-Mas, M. A. , Martín-Guerrero, J. D. , Rupérez, M. J. , Pastor, F. , Dualde, C. , Monserrat, C. , & Peris-Martínez, C. (2014). A new approach based on machine learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation. Computer Methods and Programs in Biomedicine , 116 (1), 39–47.
  • Vásquez-Morales, G. R. , Martínez-Monterrubio, S. M. , Moreno-Ger, P. , & Recio-García, J. A. (2019). Explainable prediction of chronic renal disease in the Colombian population using neural networks and case-based reasoning. IEEE Access , 7 , 152900–152910.
  • Wang, H. , Cui, Z. , Chen, Y. , Avidan, M. , Abdallah, A. B. , & Kronzer, A. (2018). Predicting hospital readmission via cost-sensitive deep learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 15 (6), 1968–1978.
  • Wang, Q. , Cao, W. , Guo, J. , Ren, J. , Cheng, Y. , & Davis, D. N. (2019). DMP_MI: An effective diabetes mellitus classification algorithm on imbalanced data With missing values. IEEE Access , 7 , 102232–102238.
  • Wu, J. , Lian, C. , Ruan, S. , Mazur, T. R. , Mutic, S. , Anastasio, M. A. , …  Li, H. (2019). Treatment outcome prediction for cancer patients based on radiomics and belief function theory. IEEE Transactions on Radiation and Plasma Medical Sciences , 3 (2), 216–224.
  • Xu, L. , He, W. , & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics , 10 (4), 2233–2243.
  • Xu, B. , Xu, L. , Cai, H. , Xie, C. , Hu, J. , & Bu, F. (2014). Ubiquitous data accessing method in IoT-based information system for emergency medical services. IEEE Transactions on Industrial Informatics , 10 (2), 1578–1586.
  • Yang, P. , & Xu, L. (2018). The Internet of Things (IoT): Informatics methods for IoT-enabled health care. Journal of Biomedical Informatics , 87 , 154–156.
  • Yin, Y. , Zeng, Y. , Chen, X. , & Fan, Y. (2016). The internet of things in healthcare: An overview. Journal of Industrial Information Integration , 1 , 3–13.
  • Yoon, H. , & Li, J. (2019). A novel positive transfer learning approach for telemonitoring of Parkinson’s disease. IEEE Transactions on Automation Science and Engineering , 16 (1), 180–191.
  • Yu, H. , Ni, J. , Dan, Y. , & Xu, S. (2012). Mining and integrating reliable decision rules for imbalanced cancer gene expression data sets. Tsinghua Science and Technology , 17 (6), 666–673.
  • Yuan, R. , Li, Z. , Guan, X. , & Xu, L. (2010). An SVM-based machine learning method for accurate internet traffic classification. Information Systems Frontiers , 12 (2), 149–156.
  • Zamani, A. , Rezaeieh, S. A. , & Abbosh, A. M. (2015). Lung cancer detection using frequency-domain microwave imaging. Electronics Letters , 51 (10), 740–741.
  • Zhang, B. , Qi, S. , Monkam, P. , Li, C. , Yang, F. , Yao, Y.-D. , & Qian, W. (2019). Ensemble learners of multiple deep CNNs for pulmonary nodules classification using CT images. IEEE Access , 7 , 110358–110371.
  • Zhang, B. , Ren, J. , Cheng, Y. , Wang, B. , & Wei, Z. (2019). Health data driven on continuous blood pressure prediction based on gradient boosting decision tree algorithm. IEEE Access , 7 , 32423–32433.
  • Zhang, J. , Lafta, R. L. , Tao, X. , Li, Y. , Chen, F. , Luo, Y. , & Zhu, X. (2017). Coupling a fast Fourier transformation With a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access , 5 , 10674–10685.
  • Zhong, H. , & Song, M. (2019). A fast exact functional test for directional association and cancer biology applications. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 16 (3), 818–826.
  • Zhong, W. , Chow, R. , & He, J. (2012). Clinical charge profiles prediction for patients diagnosed with chronic diseases using multi-level support vector machine. Expert Systems with Applications , 39 (1), 1474–1483.
  • Zięba, M. , Tomczak, J. M. , Lubicz, M. , & Świątek, J. (2014). Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients. Applied Soft Computing , 14 , 99–108.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.