Abstract
Ensemble learning is one of the powerful machine learning approaches that is generally used to strengthen models by combining the performances of several weak learners. It holds a great potential for solving umpteen problems in healthcare domain by enabling health systems to use data analytically for identifying best practices that improves healthcare and additionally reduces the cost too. The main focus of the present work is the automatic identification of erythemato-squamous disease (ESD) with higher accuracy performance, thereby, an ESD prediction system has been proposed using ensemble approach. The present study introduces GBoost (GB) ensemble framework that is based on grading approach with AdaBoost scheme for analysis and prediction of erythemato-squamous disease (ESD). The experiments were performed using dermatology dataset. The ESD prediction system uses imputation and filter approaches for data preprocessing and includes two phases for building models. In the first phase, models have been built using individual classifiers without using any ensemble technique whereas the second phase includes the GB ensemble along with dynamic base-classifiers and static meta-classifier for model building. At the end, the best classifier from phase one (without using GB ensemble framework) has been compared with the best GB ensemble set (using GB ensemble framework) from phase 2 to obtain the overall best model for ESD prediction. The proposed ESD prediction system using GB ensemble framework has achieved an accuracy of 99.45% which is higher than all the previous works on this dataset. The use of ensemble learning in this study exhibits a remarkable performance in the automatic identification of Erythemato-sequamous disease (ESD) with augmented accuracy.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41870-020-00589-4/MediaObjects/41870_2020_589_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41870-020-00589-4/MediaObjects/41870_2020_589_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41870-020-00589-4/MediaObjects/41870_2020_589_Fig3_HTML.png)
Similar content being viewed by others
References
Wootton CI, Bell S, Philavanh A et al (2018) Assessing skin disease and associated health-related quality of life in a rural Lao community. BMC Dermatol. https://doi.org/10.1186/s12895-018-0079-8
Kelbore AG, Owiti P et al (2019) Pattern of skin diseases in children attending a dermatology clinic in a referral hospital in Wolaita Sodo, southern Ethiopia. BMC Dermatol 19(5):1–8. https://doi.org/10.1186/s12895-019-0085-5
Sudha J, Murugaiyan A, Subramaniyan k (2017) Development of a mathematical model for skin disease prediction using response surface methodology. Biomed Res, Special Issue pp 355–359
Galvez JM, Castillo D et al (2018) Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series. PLoS ONE 13(5):e0196836. https://doi.org/10.1371/journal.pone.0196836
Filimon DM, Albu A (2014), Skin diseases diagnosis using artificial neural networks. In: 9th IEEE International Symposium on applied computational intelligence and informatics (2014), pp 189–194. https://doi.org/10.1109/SACI.2014.6840059
Azar AT, El-Said SA et al (2013) Linguistic hedges fuzzy feature selection for differential diagnosis of erythemato-squamous diseases. Soft Comput Appl (AISC) 195:487–500. https://doi.org/10.1007/978-3-642-33941-7_43
Sivasankari S, Jacob SG (2016) Investigation on the performance of classifiers in prediction of erythemato-squamous disease: an automated ontology learning (AOL) methodology. Middle-East J Sci Res 24(8):2567–2576. https://doi.org/10.5829/idosi.mejsr.2016.24.08.23829
Chakraborti S, Choudhary A, Singh A et al (2018) A machine learning based method to detect epilepsy. Int J Inf Technol 10:257–263. https://doi.org/10.1007/s41870-018-0088-1
Herland M, Khoshgoftaar TM, Wald R (2014) A review of data mining using big data in health informatics. J Big Data 1(2):1–35. https://doi.org/10.1186/2196-1115-1-2
Yan J (2018) Suqing H (2018) Classifying imbalanced data sets by a novel re-sample and cost-sensitive stacked generalization method. Math Probl Eng 15:18. https://doi.org/10.1155/2018/5036710
Vitorino D, Coelho ST, Santos P, et al (2014) A random forest algorithm applied to condition-based wastewater deterioration modeling and forecasting. In: 16th Conference on water distribution system analysis, WDSA 2014 (2014), pp 401–410. https://doi.org/10.1016/j.proeng.2014.11.205
**e J, **e W, Wang C, Gao X (2010) A novel hybrid feature selection method based on IFSFFS and SVM for the diagnosis of erythemato-squamous diseases. In: JMLR: Workshop and Conference Proceedings 11, pp 142–151
Kecman V, Kikec M (2010) Erythemato-squamous diseases diagnosis by support vector machines and RBF NN. In: ICAISC 2010, Part I, LNAI 6113 (2010) 613–620. https://doi.org/https://doi.org/10.1007/978-3-642-13208-7_76
Wei L, Gan Q, Ji T (2018) Skin disease recognition method based on image color and texture features. Comput MathMethods in Med. https://doi.org/10.1155/2018/8145713
Kadhim QK (2017) Classification of human skin diseases using data mining. Int J Adv Eng Res Sci 4(1):159–163. https://doi.org/10.22161/ijaers.4.1.25
Pal AKVS, Kumar S (2019) Classification of skin disease using ensemble data mining techniques. Asian Pac J Cancer Prev. https://doi.org/10.31557/APJCP.2019.20.6.1887
Putatunda S (2019), A hybrid deep learning approach for diagnosis of the erythemato-squamous disease. ar**v:1909.07587v1, pp1–13.
Olatunji SO, Arif H (2013) Identification of erythemato-squamous skin diseases using extreme learning machine and artificial neural network. ICTACT J Soft Comput. https://doi.org/10.21917/ijsc.2013.0090
Maghooli K, Langarizadeh M, Shahmoradi L et al (2016) Differential diagnosis of erythmato-squamous diseases using classification and regression tree. Acta Inf Med 24(5):338–342. https://doi.org/10.5455/aim.2016.24.338-342
Kopec KG, Nowak L, Ogorzalek M (2015) Automatic diagnosis of melanoid skin lesions using machine learning methods. In: ICAISC 2015, Part I, LNAI 9119 (2015), pp 577–585. https://doi.org/10.1007/978-3-319-19324-3_51
Hameed N, Hameed F, Shabut A et al (2019) An intelligent computer-aided scheme for classifying multiple skin lesions. Computers. https://doi.org/10.3390/computers8030062
Revett K, Gorunescu F, Salem AB, et al (2009) Evaluation of the feature space of an erythemato squamous dataset using rough sets, Annals of University of Craiova, Mathematics and Computer Science Series (2009), 36(2):123–130. http://inf.ucv.ro/~ami/index.php/ami/article/view/294/285
Gupta C, Gondhi NK, Lehana PK (2019) Analysis and identification of dermatological diseases using gaussian mixture modeling. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2929857
Gerhana YA, Zulfikar WB, Ramdani AH (2018) Implementation of nearest neighbor using HSV to identify skin disease. IOP Conf Ser Mater Sci Eng 288:1–5. https://doi.org/10.1088/1757-899X/288/1/012153
Zhang X, Wang S, Liu J, Tao C (2018) Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Med Inf Decis Mak. https://doi.org/10.1186/s12911-018-0631-9
Danjuma K, Osofisan AO (2014) evaluation of predictive data mining algorithms in erythemato-squamous disease diagnosis. IJCSI Int J Comput Sci Issues 11(6):85–94
Jeddi FR, Arabfard M, Arabkermany Z, Gilasi H (2016) The diagnostic value of skin disease diagnosis expert system. Acta Inf Med. https://doi.org/10.5455/aim.2016.24.30-33
Tuba E, Ribic I, Capor-Hrosikc R, Tuba M (2017) Support Vector machine optimized by elephant herding algorithm for erythemato-squamous diseases detection, information technology and quantitative management (ITQM 2016). Pro Comput Sci 122:916–923. https://doi.org/10.1016/j.procs.2017.11.455
Maryam NA, Setiawan O, Wahyunggoro (2017) A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease. In: International Conference on mathematics: pure, applied and computation, 2017. https://doi.org/10.1063/1.4994451
Guvenir HA, Ilter N (2020) UCI repository of machine learning databases. Irvine, CA: University of California. https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/. Accessed 30 March 2020
Sun M, Min T, Zang T, Wang Y (2019) CDL4CDRP: a collaborative deep learning approach for clinical decision and risk prediction. Processes. https://doi.org/10.3390/pr7050265
Jha SKR, Pan Z, Elahi E, Patel N (2019) A comprehensive search for expert classification methods in disease diagnosis and prediction. Expert Syst. https://doi.org/10.1111/exsy.12343
Menai MEB, Altayash N (2014) Differential diagnosis of erythemato-squamous diseases using ensemble of decision trees. In: International Conference on industrial, engineering and other applications of applied intelligent systems, IEA/AIE (2014), pp 369–377. https://doi.org/10.1007/978-3-319-07467-2_39
Bhosle U, Deshmukh J (2019) Mammogram classification using AdaBoost with RBFSVM and Hybrid KNN–RBFSVM as base estimator by adaptively adjusting γ and C value. Int J Inf Technol 11:719–726. https://doi.org/10.1007/s41870-018-0241-x
Yang L (2011) Advanced in Control Engineering and Information Science Classifiers selection for ensemble learning based on accuracy and diversity. Proc Eng 15:4266–4270. https://doi.org/10.1016/j.proeng.2011.08.800
Mironczuk MM, Protasiewicz J (2018) A recent overview of the state-of-the-art elements of text classification. Expert Syst Appl 106:36–54. https://doi.org/10.1016/j.eswa.2018.03.058
Bryll R, Osunab RG, Quek F (2003) Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recogn 36:1291–1302. https://doi.org/10.1016/S0031-3203(02)00121-8
Halawani SM, Ahmad A (2012) Ensemble methods for prediction of Parkinson disease. In: International Conference on intelligent data engineering and automated learning (IDEAL 2012), pp 516–521. https://doi.org/10.1007/978-3-642-32639-4_63
Souto MC, Jaskowiak PA, Costa IG (2015) Impact of missing data imputation methods on gene expression clustering and classification. BMC Bioinform. https://doi.org/10.1186/s12859-015-0494-3
Samet S, Ishraque MT, Ghadamyari M, Kakadiya K, Mistry Y (2019) TouchMetric: a machine learning based continuous authentication feature testing mobile application. Int J Inf Technol 11:625–631. https://doi.org/10.1007/s41870-019-00306-w
Fong S, Zhuang Y, Fister I et al (2013) A biometric authentication model using hand gesture images. BioMed Eng OnLine 12(111):1–18. https://doi.org/10.1186/1475-925X-12-111
Ali L, Rahman A, Khan A et al (2019) An automated diagnostic system for heart disease prediction based on 2 statistical model and optimally configured deep neural network. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2904800
Saito T, Rehmsmeier M (2015) the precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10(3):e0118432. https://doi.org/10.1371/journal.pone.0118432 ((1-21))
Guvenir HA, Demiröz G, Ilter N (1998) Learning differential diagnosis of Erythemato-Squamous diseases using voting feature intervals. Aritif Intell Med 13(3):147–165. https://doi.org/10.1016/s0933-3657(98)00028-1
Guvenir HA, Emeksiz N (2000) An expert system for the differential diagnosis of erythemato-squamous diseases. Expert Syst Appl 18:43–49. https://doi.org/10.1016/S0957-4174(99)00049-4
Ubeyli ED, Guler I (2005) Automatic detection of erythemato squmous diseases using adaptive neuro-fuzzy inference systems. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2004.03.003
Polat K, Gunes S (2009) A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2007.11.051
Ubeyli ED (2009) Combined neural networks for diagnosis of erythemato-squamous diseases. Expert Syst Appl 36(3):5107–5112. https://doi.org/10.1016/j.eswa.2008.06.002
Ubeyli ED, Dogdu E (2010) Automatic detection of erythemato-squamous diseases using k-means clustering. J Med Syst 34(2):179–184. https://doi.org/10.1007/s10916-008-9229-6
Lekkas S, Mikhailov L (2010) Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artif Intell Med 50(2):117–126. https://doi.org/10.1016/j.artmed.2010.05.007
Giveki D, Salimi H, Bitaraf AA, Khademian Y (2011) Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVM. Signal Image Process Int J. https://doi.org/10.5121/sipij.2011.240657
Elsayad AM, AlDhaifallah M, Nassef AMA (2018) Analysis and diagnosis of erythemato-squamous diseases using CHAID decision trees. In: 2018 15th International Multi-Conference on systems, signals & devices, 2018, pp 252–262. https://doi.org/10.1109/ssd.2018.8570553
Idoko JB, Arslan M, Abiyev R (2018) Fuzzy neural system application to differential diagnosis of erythemato-squamous diseases. Cyprus J Med Sci. https://doi.org/10.5152/cjms.2018.576
Funding
No financial support was received for this work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest in this work.
Rights and permissions
About this article
Cite this article
Shastri, S., Kour, P., Kumar, S. et al. GBoost: A novel Grading-AdaBoost ensemble approach for automatic identification of erythemato-squamous disease. Int. j. inf. tecnol. 13, 959–971 (2021). https://doi.org/10.1007/s41870-020-00589-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-020-00589-4