Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction
Tóm tắt
Từ khóa
Tài liệu tham khảo
Garcia-Molina, L., Lewis-Mikhael, A.M., Riquelme-Gallego, B., Cano-Ibanez, N., Oliveras-Lopez, M.J., Bueno-Cavanillas, A.: Improving type 2 diabetes mellitus glycaemic control through lifestyle modification implementing diet intervention: a systematic review and meta-analysis. Eur. J. Nutr. 59(4), 1313–1328 (2020)
Liang, Y.Z., Li, J.J.H., Xiao, H.B., He, Y., Zhang, L., Yan, Y.X.: Identification of stress-related microRNA biomarkers in type 2 diabetes mellitus: a systematic review and meta-analysis. J. Diabetes 12(9), 633–644 (2020)
Zhang, Y., Pan, X.F., Chen, J., Xia, L., Cao, A., Zhang, Y., et al.: Combined lifestyle factors and risk of incident type 2 diabetes and prognosis among individuals with type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies. Diabetologia 63(1), 21–33 (2020)
Wong, J.J., Addala, A., Abujaradeh, H., Adams, R.N., Barley, R.C., Hanes, S.J., et al.: Depression in context: Important considerations for youth with type 1 vs type 2 diabetes. Pediatr. Diabetes 21(1), 135–142 (2020)
Australia, Healthdirect. Type 2 diabetes (2022). https://www.healthdirect.gov.au/type-2-diabetes
Abdollahi, J., Moghaddam, B.N., Parvar, M.E.: Improving diabetes diagnosis in smart health using genetic-based Ensemble learning algorithm. Approach to IoT Infrastructure. Future Gen. Distrib. Syst. J. 1, 23–30 (2019)
Wang, S., Ma, P., Zhang, S., Song, S., Wang, Z., Ma, Y., et al.: Fasting blood glucose at admission is an independent predictor for 28-day mortality in patients with COVID-19 without previous diagnosis of diabetes: a multi-centre retrospective study. Diabetologia (2020). https://doi.org/10.1007/s00125-020-05209-1
Debata, P.P., Mohapatra, P.: Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model. J. Integr. Bioinform. 18, 81–99 (2020)
Hossain, M.S., Muhammad, G.: Cloud-assisted industrial internet of things (iiot)–enabled framework for health monitoring. Comput. Netw. 101, 192–202 (2016)
Temko, A.: Accurate wearable heart rate monitoring during physical exercises using PPG. IEEE Trans. Biomed. Eng. (2017). https://doi.org/10.1109/TBME.2017.2676243
Abawajy, J.H., Hassan, M.M.: Federated internet of things and cloud computing pervasive patient health monitoring system. IEEE Commun. Mag. 55(1), 48–53 (2017)
La, H.J.: A conceptual framework for trajectory-based medical analytics with IoT contexts. J. Comput. Syst. Sci. 82(4), 610–626 (2016)
Rahmani, A.M., Gia, T.N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., Liljeberg, P.: Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach. Futur. Gener. Comput. Syst. 78, 641–658 (2017)
Verma, P., Sood, S.K.: Cloud-centric IoT based disease diagnosis healthcare framework. J Parallel Distrib. Comput. 116, 27–38 (2018)
Din, I.U., Guizani, M., Rodrigues, J.J., Hassan, S., Korotaev, V.V.: Machine learning in the Internet of Things: designed techniques for smart cities. Futur. Gener. Comput. Syst. 100, 826–843 (2019)
International Warfarin Pharmacogenetics C, Klein, T.E., Altman, R.B., Eriksson, N., Gage, B.F., Kimmel, S.E., et al.: Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360(8), 753–764 (2009). (pmid:19228618; PubMed Central PMCID: PMCPMC2722908)
Hu, Y.H., Wu, F., Lo, C.L., Tai, C.T.: Predicting warfarin dosage from clinical data: a supervised learning approach. Artif. Intell. Med. 56(1), 27–34 (2012). (Epub 2012/04/28. pmid:22537823)
Tao, Y., Chen, Y.J., Fu, X., Jiang, B., Zhang, Y.: Evolutionary ensemble learning algorithm to modeling warfarin dose prediction for Chinese. IEEE J. Biomed. Health Inform. 23(1), 395–406 (2018). (Epub 2018/07/12. pmid:29993619)
Wang, S.Q., Yang, J., Chou, K.C.: Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. J. Theor. Biol. 242(4), 941–946 (2006). (Epub 2006/06/30. pmid:16806277)
Palimkar, P., Shaw, R. N., Ghosh, A.: Machine learning technique to prognosis diabetes disease: random forest classifier approach. In: Advanced Computing and Intelligent Technologies, pp. 219–244. Springer, Singapore (2022)
Ahmad, H.F., Mukhtar, H., Alaqail, H., Seliaman, M., Alhumam, A.: Investigating health-related features and their impact on the prediction of diabetes using machine learning. Appl. Sci. 11(3), 1173 (2021)
Li, J., Yuan, P., Hu, X., Huang, J., Cui, L., Cui, J., et al.: A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J. Biomed. Inform. 115, 103693 (2021)
Dietterich, T.G.: Ensemble learning. Handb. Brain Theory Neural Netw. 2(1), 110–125 (2002)
Sagi, O., Rokach, L.: Ensemble learning: A survey. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 8(4), e1249 (2018)
Fatima, M., Pasha, M.: Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 9(01), 1 (2017)
Alić, B., Gurbeta, L., Badnjević, A.: Machine learning techniques for classification of diabetes and cardiovascular diseases. In: Embedded Computing (MECO), 2017 6th Mediterranean Conference on, pp. 1–4. IEEE (2017)
Kumar, A., Kumar, P., Srivastava, A., Kumar, V. A., Vengatesan, K., Singhal, A.: Comparative analysis of data mining techniques to predict heart disease for diabetic patients. In: International Conference on Advances in Computing and Data Sciences, pp. 507–518. Springer, Singapore (2020)
Saru, S., Subashree, S.: Analysis and prediction of diabetes using machine learning. Int. J. Emerg. Tech. Innov. Eng. 5(4) (2019)
Subramaniyan, S., Regan, R., Perumal, T., Venkatachalam, K.: Semi-supervised machine learning algorithm for predicting diabetes using big data analytics. In: Business Intelligence for Enterprise Internet of Things, pp. 139–149. Springer, Cham (2020)
Yang, H., Luo, Y., Ren, X., Wu, M., He, X., Peng, B., et al.: Risk prediction of diabetes: big data mining with fusion of multifarious physical examination indicators. Inform. Fusion 75, 140–149 (2021)
Beunza, J.J., Puertas, E., García-Ovejero, E., Villalba, G., Condes, E., Koleva, G., et al.: Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J. Biomed. Inform. 97, 103257 (2019)
Zeki, A. M., Taha, R., Alshakrani, S.: Developing a predictive model for diabetes using data mining techniques. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 24–28. IEEE (2021)
Kalyankar, G. D., Poojara, S. R., Dharwadkar, N. V.: Predictive analysis of diabetic patient data using machine learning and Hadoop. In: 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC), pp. 619–624. IEEE (2017)
Rghioui, A., Naja, A., Oumnad, A.: Diabetic patients monitoring and data classification using IoT application. In: 2020 International Conference on Electrical and Information Technologies (ICEIT), pp. 1–6. IEEE (2020).
Joshi, T.N., Chawan, P.P.M.: Diabetes prediction using machine learning techniques. Ijera 8(1), 9–13 (2018)
Sikder, N., Masud, M., Bairagi, A.K., Arif, A.S.M., Nahid, A.A., Alhumyani, H.A.: Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry 13(4), 670 (2021)
Ihnaini, B., Khan, M.A., Khan, T.A., Abbas, S., Daoud, M.S., Ahmad, M., Khan, M.A.: A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Comput. Intell. Neurosci. (2021). https://doi.org/10.1155/2021/4243700
Banchhor, M., Singh, P.: Comparative study of ensemble learning algorithms on early stage diabetes risk prediction. In: 2021 2nd International Conference for Emerging Technology (INCET), pp. 1–6. IEEE (2021)
Sabbir, M. M. H., Sayeed, A., Jamee, M. A. U. Z.: Diabetic retinopathy detection using texture features and ensemble learning. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 178–181. IEEE (2020)
Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., Stiglic, G.: Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci. Rep. 10(1), 1–12 (2020)
Muhammad, L.J., Algehyne, E.A., Usman, S.S.: Predictive supervised machine learning models for diabetes mellitus. SN Comput. Sci. 1(5), 1–10 (2020)
Abdulhadi, N., Al-Mousa, A.: Diabetes detection using machine learning classification methods. In: 2021 International Conference on Information Technology (ICIT), pp. 350–354. IEEE (2021)
Naz, H., Ahuja, S.: Deep learning approach for diabetes prediction using PIMA Indian dataset. J. Diabetes Metab. Disord. 19(1), 391–403 (2020)
Al-Zebari, A., Sengur, A.: Performance comparison of machine learning techniques on diabetes disease detection. In: 2019 1st international informatics and software engineering conference (UBMYK), pp. 1–4. IEEE (2019)
Rawat, V., Suryakant, S.: A classification system for diabetic patients with machine learning techniques. Int. J. Math. Eng. Manage. Sci. 4(3), 729–744 (2019)
Abdollahi, J., Nouri-Moghaddam, B., Ghazanfari, M.: Deep neural network based ensemble learning algorithms for the healthcare system (diagnosis of chronic diseases). arXiv preprint arXiv:2103.08182 (2021)
Abdollahi, J., Nouri-Moghaddam, B., Ghazanfari, M.: Deep neural network based ensemble learning algorithms for the healthcare system (diagnosis of chronic diseases) (2021). arXiv preprint arXiv:2103.08182
Priyanka, N.A., Kumar, D.: Decision tree classifier: a detailed survey. Int. J. Inf. Decis. Sci. 12(3), 246–269 (2020)
Jadhav, S.D., Channe, H.P.: Comparative study of K-NN, naive Bayes and decision tree classification techniques. Int. J. Sci. Res. 5(1), 1842–1845 (2016)
Karthikeyan, T., Thangaraju, P.: PCA-NB algorithm to enhance the predictive accuracy. Int. J. Eng. Tech 6(1), 381–387 (2014)
Amato, F., López, A., Peña-Méndez, E.M., Vaňhara, P., Hampl, A., Havel, J.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. (2013). https://doi.org/10.2478/v10136-012-0031-x
Vijayarani, S., Dhayanand, S., Phil, M.: Kidney disease prediction using SVM and ANN algorithms. Int. J Comput. Bus. Res. (IJCBR) 6(2), 1–12 (2015)
Platt, J. C.: 12 fast training of support vector machines using sequential minimal optimization. Adv. Kernel Methods 185–208 (1999)
Ruggieri, S.: Efficient C4.5 [classification algorithm]. IEEE Trans. Knowl. Data Eng. 14(2), 438–444 (2002)
Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Izquierdo-Verdiguier, E., Zurita-Milla, R.: An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing. Int. J. Appl. Earth Obs. Geoinf. 88, 102051 (2020)
Zhang, Z.: Introduction to machine learning: k-nearest neighbors. Ann. Transl. Med. 4(11), 218 (2016)
Naimi, A., Balzer, L. B.: Stacked generalization: an introduction to super learning. bioRxiv 172395 (2017)
Bui, D.T., Ho, T.C., Pradhan, B., Pham, B.T., Nhu, V.H., Revhaug, I.: GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ. Earth Sci. 75(14), 1101 (2016)
Del Jesus, M.J., Hoffmann, F., Navascués, L.J., Sánchez, L.: Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans. Fuzzy Syst. 12(3), 296–308 (2004)
Kearns, M.: Boosting theory towards practice: Recent developments in decision tree induction and the weak learning framework. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1337–1339 (1996)
Choi, S., Kim, Y. J., Briceno, S., Mavris, D.: Prediction of weather-induced airline delays based on machine learning algorithms. In: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), pp. 1–6. IEEE (2016)
Schapire, R. E.: The boosting approach to machine learning: An overview. In: Nonlinear estimation and classification, pp. 149–171. Springer, New York (2003)
Barik, S., Mohanty, S., Mohanty, S., Singh, D.: Analysis of prediction accuracy of diabetes using classifier and hybrid machine learning techniques. In: Intelligent and Cloud Computing, pp. 399–409. Springer, Singapore (2021)
Sewell, M.: Ensemble learning. RN 11(02), 1–34 (2008)
Singh, N., Singh, P.: A stacked generalization approach for diagnosis and prediction of type 2 diabetes mellitus. In: Computational Intelligence in Data Mining, pp. 559–570. Springer, Singapore (2020)
Singh, N., Singh, P.: Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus. Biocybern. Biomed. Eng. 40(1), 1–22 (2020)
Kuo, K.M., Talley, P., Kao, Y., Huang, C.H.: A multi-class classification model for supporting the diagnosis of type II diabetes mellitus. PeerJ 8, e9920 (2020)
Bernardini, M., Morettini, M., Romeo, L., Frontoni, E., Burattini, L.: Early temporal prediction of type 2 diabetes risk condition from a general practitioner electronic health record: a multiple instance boosting approach. Artif. Intell. Med. 105, 101847 (2020)
Mahendran, N., Vincent, P.D.R., Srinivasan, K., Sharma, V., Jayakody, D.K.: Realizing a stacking generalization model to improve the prediction accuracy of major depressive disorder in adults. IEEE Access 8, 49509–49522 (2020)
Khan, F.A., Zeb, K., Alrakhami, M., Derhab, A., Bukhari, S.A.C.: Detection and prediction of diabetes using data mining: a comprehensive review. IEEE Access (2021). https://doi.org/10.1109/ACCESS.2021.3059343
Mirjalili, S.: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence, vol. 780. Springer, Cham (2019)
Mathew, T. V.: Genetic algorithm. Report submitted at IIT Bombay (2012)
Abdollahi, J., Keshandehghan, A., Gardaneh, M., Panahi, Y., Gardaneh, M.: accurate detection of breast cancer metastasis using a hybrid model of artificial intelligence algorithm. Arch. Breast Cancer (2020). https://doi.org/10.32768/abc.20207118-24
Zadeh, H.G., Jamshidi, H., Fayazi, A., Gholizadeh, M.H., Toussi, C.A., Danaeian, M.: A new model for retinal lesion detection of diabetic retinopathy using hierarchical self-organizing maps. Iran J. Comput. Sci. 3(2), 93–101 (2020)
Bagherzadeh, J., Asil, H.: A review of various semi-supervised learning models with a deep learning and memory approach. Iran J. Comput. Sci. 2(2), 65–80 (2019)
Iqbal, M.S., Luo, B., Khan, T., Mehmood, R., Sadiq, M.: Heterogeneous transfer learning techniques for machine learning. Iran J. Comput. Sci. 1(1), 31–46 (2018)
Singh, P. P., Prasad, S., Das, B., Poddar, U., Choudhury, D. R.: Classification of diabetic patient data using machine learning techniques. In: Ambient Communications and Computer Systems, pp. 427–436. Springer, Singapore (2018)
Bhuvaneswari, G., Manikandan, G.: A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm. Computing 100(8), 759–772 (2018)
Carrera, E.V., González, A., Carrera, R.: Automated detection of diabetic retinopathy using SVM. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1–4, August 2017. IEEE (2017). https://doi.org/10.1109/INTERCON.2017.8079692
Sharma, P., Saxena, S., Sharma, Y. M.: An efficient decision support model based on ensemble framework of data mining features assortment & classification process. In: 2018 3rd International Conference on Communication and Electronics Systems (ICCES), pp. 487–491. IEEE (2018)