{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T12:37:50Z","timestamp":1770986270622,"version":"3.50.1"},"reference-count":35,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Millions of people across the world are suffering from diabetic retinopathy. This disease majorly affects the retina of the eye, and if not identified priorly causes permanent blindness. Hence, detecting diabetic retinopathy at an early stage is very important to safeguard people from blindness. Several machine learning (ML) algorithms are implemented on the dataset of diabetic retinopathy available in the UCI ML repository to detect the symptoms of diabetic retinopathy. But, most of those algorithms are implemented individually. Hence, this article proposes an effective integrated ML approach that uses the support vector machine (SVM), principal component analysis (PCA), and moth-flame optimization techniques. Initially, the ML algorithms decision tree (DT), SVM, random forest (RF), and Na\u00efve Bayes (NB) are applied to the diabetic retinopathy dataset. Among these, the SVM algorithm is outperformed with an average of 76.96% performance. Later, all the aforementioned ML algorithms are implemented by integrating the PCA technique to reduce the dimensions of the dataset. After integrating PCA, it is noticed that the performance of the algorithms NB, RF, and SVM is reduced dramatically; on the contrary, the performance of DT is increased. To improve the performance of ML algorithms, the moth-flame optimization technique is integrated with SVM and PCA. This proposed approach is outperformed with an average of 85.61% performance among all the other considered ML algorithms, and the classification of class labels is achieved correctly.<\/jats:p>","DOI":"10.1515\/comp-2020-0222","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T07:36:51Z","timestamp":1646897811000},"page":"83-91","source":"Crossref","is-referenced-by-count":7,"title":["An effective integrated machine learning approach for detecting diabetic retinopathy"],"prefix":"10.1515","volume":"12","author":[{"given":"Penikalapati","family":"Pragathi","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology (VIT) , Vellore , Tamil Nadu , India"}]},{"given":"Agastyaraju","family":"Nagaraja Rao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology (VIT) , Vellore , Tamil Nadu , India"}]}],"member":"374","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"2022081707553240065_j_comp-2020-0222_ref_001","doi-asserted-by":"crossref","unstructured":"C. S. Dangare and S. S. Apte, \u201cImproved study of heart disease prediction system using data mining classification techniques,\u201d Int J Comput Appl., vol. 47, no. 10, pp. 44\u201348, 2012.","DOI":"10.5120\/7228-0076"},{"key":"2022081707553240065_j_comp-2020-0222_ref_002","doi-asserted-by":"crossref","unstructured":"J. D. Elia, J. K. Sun, and W. S. Alan, \u201cDiabetic retinopathy: current understanding mechanisms, and treatment strategies,\u201d JCI Insight, vol. 2, pp. 1\u201313, 2017.","DOI":"10.1172\/jci.insight.93751"},{"key":"2022081707553240065_j_comp-2020-0222_ref_003","unstructured":"M. I. Al-janabi, M. H. Qutqut, and M. Hijjawi, \u201cMachine learning classification techniques for heart disease prediction: a review,\u201d Int J Eng Technol., vol. 7, pp. 5373\u20135379, 2018."},{"key":"2022081707553240065_j_comp-2020-0222_ref_004","doi-asserted-by":"crossref","unstructured":"A. W. Zebene, A. Eirik, T. Botsis, D. Albers, M. Lena, and H. Gunnar, \u201cData-driven blood glucose pattern classification and anomalies detection: machine-learning applications in type 1 diabetes,\u201d J Med Internet Res., vol. 21, pp. 1\u201318, 2019.","DOI":"10.2196\/11030"},{"key":"2022081707553240065_j_comp-2020-0222_ref_005","doi-asserted-by":"crossref","unstructured":"S. L. M. Sainte, A. Linah, A. Rana, and T. Saba, \u201cCurrent techniques of diabetes prediction: review and case study,\u201d Appl. Sci., vol. 9, pp. 1\u201319, 2019.","DOI":"10.3390\/app9214604"},{"key":"2022081707553240065_j_comp-2020-0222_ref_006","doi-asserted-by":"crossref","unstructured":"A. Javeria, M. Sharif, M. Yasmin, H. Ali, and S. F. Lawrence, \u201cA method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions,\u201d J. Comput. Sci., vol. 19, pp. 153\u2013164, 2017.","DOI":"10.1016\/j.jocs.2017.01.002"},{"key":"2022081707553240065_j_comp-2020-0222_ref_007","doi-asserted-by":"crossref","unstructured":"F. Cut, E. M. Sipayung, and M. Siti, \u201cAnalysis and prediction of diabetes complication disease using data mining algorithm,\u201d Proc. Comput. Sci., vol. 161, pp. 449\u2013457, 2019.","DOI":"10.1016\/j.procs.2019.11.144"},{"key":"2022081707553240065_j_comp-2020-0222_ref_008","doi-asserted-by":"crossref","unstructured":"G. Sumalatha and N. J. R. Muniraj, \u201cSurvey on medical diagnosis using data mining techniques,\u201d International Conference on Optical Imaging Sensor and Security, Coimbatore, India, 2013.","DOI":"10.1109\/ICOISS.2013.6678433"},{"key":"2022081707553240065_j_comp-2020-0222_ref_009","doi-asserted-by":"crossref","unstructured":"R. Ghosh, G. Kuntal, and S. Maitra, \u201cAutomatic detection and classification of diabetic retinopathy stages using CNN,\u201d International Conference on Signal Processing and Integrated Networks, Noida, India, 2017.","DOI":"10.1109\/SPIN.2017.8050011"},{"key":"2022081707553240065_j_comp-2020-0222_ref_010","unstructured":"A. E. Ahmed, A. T. Sahlol, and A. A. Mohamed, \u201cA bio-inspired Moth-flame optimization algorithm for Arabic handwritten letter recognition,\u201d International Conference on Control Artificial Intelligence, Robotics & Optimization, Prague, Czech Republic, 2017."},{"key":"2022081707553240065_j_comp-2020-0222_ref_011","doi-asserted-by":"crossref","unstructured":"C. Zhu, C. I. Uwa, and W. Feng, \u201cImproved logistic regression model for diabetes prediction by integrating PCA and K-means techniques,\u201d Informatic Med Unlocked, vol. 17, pp. 1\u20137, 2019.","DOI":"10.1016\/j.imu.2019.100179"},{"key":"2022081707553240065_j_comp-2020-0222_ref_012","doi-asserted-by":"crossref","unstructured":"G. T. Reddy, M. P. K. Reddy, K. Lakshmanna, R. Kaluri, D. S. Rajput, G. Srivastava, et al., \u201cAnalysis of dimensionality reduction techniques on big data,\u201d IEEE Access, vol. 8, pp. 54776\u201354788, 2020.","DOI":"10.1109\/ACCESS.2020.2980942"},{"key":"2022081707553240065_j_comp-2020-0222_ref_013","doi-asserted-by":"crossref","unstructured":"T. R. Gadekallu, N. Khare, S. Bhattacharya, S. Singh, P. K. R. Maddikunta, I. H. Ra, et al., \u201cEarly detection of diabetic retinopathy using PCA-firefly-based deep learning model,\u201d Electronics, vol. 9, pp. 1\u201316, 2020.","DOI":"10.3390\/electronics9020274"},{"key":"2022081707553240065_j_comp-2020-0222_ref_014","doi-asserted-by":"crossref","unstructured":"S. Bhattacharya, S. R. K. S, P. K. R. Maddikunta, R. Kaluri, S. Singh, T. R. Gadekallu, et al., \u201cA novel PCA-firefly-based XGBoost classification model for intrusion detection in networks using GPU,\u201d Electronics, vol. 9, pp. 1\u201316, 2020.","DOI":"10.3390\/electronics9020219"},{"key":"2022081707553240065_j_comp-2020-0222_ref_015","doi-asserted-by":"crossref","unstructured":"J. S. Salimi, M. Z. Hossein, and K. Mozafari, \u201cHepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA),\u201d Comput. Meth. Prog. Bio., vol. 108, pp. 570\u2013579, 2012.","DOI":"10.1016\/j.cmpb.2011.08.003"},{"key":"2022081707553240065_j_comp-2020-0222_ref_016","doi-asserted-by":"crossref","unstructured":"L. Shen, H. Chen, Z. Yu, W. Kang, B. Zhang, H. Li, et al., \u201cEvolving support vector machines using fruit fly optimization for medical data classification,\u201d Knowledge-Based Syst., vol. 96, pp. 61\u201375, 2016.","DOI":"10.1016\/j.knosys.2016.01.002"},{"key":"2022081707553240065_j_comp-2020-0222_ref_017","doi-asserted-by":"crossref","unstructured":"S. Poornima, S. Singh, and G. S. J. Pandi, \u201cEffective heart disease prediction system using data mining techniques,\u201d Int. J. Nanomed., vol. 13, pp. 121\u2013124, 2018.","DOI":"10.2147\/IJN.S124998"},{"key":"2022081707553240065_j_comp-2020-0222_ref_018","doi-asserted-by":"crossref","unstructured":"H. D. Jude, D. Omer, and U. Kose, \u201cAn enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network,\u201d Intell. Biomed. Data Anal. Process., vol. 32, pp. 707\u2013721, 2019.","DOI":"10.1007\/s00521-018-03974-0"},{"key":"2022081707553240065_j_comp-2020-0222_ref_019","doi-asserted-by":"crossref","unstructured":"T. Mahboob Alam, M. A. Iqbal, Y. Ali, A. Wahab, S. Ijaz, T. Imtiaz Baig, et al., \u201cA model for early prediction of diabetes,\u201d Informatics Med. Unlocked, vol. 16, pp. 1\u20136, 2019.","DOI":"10.1016\/j.imu.2019.100204"},{"key":"2022081707553240065_j_comp-2020-0222_ref_020","doi-asserted-by":"crossref","unstructured":"H. Chirath and C. Charith, \u201cA Machine learning approach to predict diabetes using short recorded photoplethysmography and physiological characteristics,\u201d Artif. Intell. Med., vol. 11526, pp. 322\u2013327, 2019.","DOI":"10.1007\/978-3-030-21642-9_41"},{"key":"2022081707553240065_j_comp-2020-0222_ref_021","unstructured":"W. Mitesh, V. Kumar, S. Tarale, G. Payal, and D. J. Chaudhari, \u201cDiabetes diagnosis using machine learning algorithms,\u201d Int. Res. J. Eng. Technol., vol. 6, pp. 1470\u20131476, 2019."},{"key":"2022081707553240065_j_comp-2020-0222_ref_022","doi-asserted-by":"crossref","unstructured":"G. Rishab and T. Leng, \u201cAutomated identification of diabetic retinopathy using deep learning,\u201d Ophthalmology, vol. 124, pp. 962\u2013969, 2017.","DOI":"10.1016\/j.ophtha.2017.02.008"},{"key":"2022081707553240065_j_comp-2020-0222_ref_023","doi-asserted-by":"crossref","unstructured":"S. Qummar, F. G. Khan, S. Shah, A. Khan, S. Shamshirband, Z. U. Rehman, et al., \u201cA deep learning ensemble approach for diabetic retinopathy detection,\u201d IEEE Access, vol. 7, pp. 150530\u2013150539, 2019.","DOI":"10.1109\/ACCESS.2019.2947484"},{"key":"2022081707553240065_j_comp-2020-0222_ref_024","doi-asserted-by":"crossref","unstructured":"J. Sahlsten, J. Jaskari, J. Kivinen, L. Turunen, E. Jaanio, K. Hietala, et al., \u201cDeep learning fundus image analysis for diabetic retinopathy and macular edema grading,\u201d Sci. Rep., vol. 9, pp. 1\u201311, 2019.","DOI":"10.1038\/s41598-019-47181-w"},{"key":"2022081707553240065_j_comp-2020-0222_ref_025","doi-asserted-by":"crossref","unstructured":"R. A. Welikala, M. M. Fraz, J. Dehmeshki, A. Hoppe, V. Tah, S. Mann, et al., \u201cGenetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy,\u201d Computerize. Med. Imag. Graphic., vol. 43, pp. 64\u201377, 2015.","DOI":"10.1016\/j.compmedimag.2015.03.003"},{"key":"2022081707553240065_j_comp-2020-0222_ref_026","doi-asserted-by":"crossref","unstructured":"C. G. Babu and S. P. Shantharajah, \u201cAn optimized feature selection based on genetic approach and support vector machine for heart disease,\u201d Cluster Comput., vol. 22, pp. 14777\u201314787, 2019.","DOI":"10.1007\/s10586-018-2416-4"},{"key":"2022081707553240065_j_comp-2020-0222_ref_027","doi-asserted-by":"crossref","unstructured":"T. G. Reddy, M. K. R. Praveen, L. Kuruva, R. D. Singh, K. Rajesh, and S. Gautam, \u201cHybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis,\u201d Evolut. Intell., vol. 13, pp. 185\u2013196, 2019.","DOI":"10.1007\/s12065-019-00327-1"},{"key":"2022081707553240065_j_comp-2020-0222_ref_028","doi-asserted-by":"crossref","unstructured":"B. Antal and H. Andras, \u201cAn ensemble-based system for automatic screening of diabetic retinopathy,\u201d Knowledge-Based Syst., vol. 60, pp. 20\u201327, 2014.","DOI":"10.1016\/j.knosys.2013.12.023"},{"key":"2022081707553240065_j_comp-2020-0222_ref_029","doi-asserted-by":"crossref","unstructured":"M. Seyedali, \u201cMoth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,\u201d Knowledge-Based Syst., vol. 89, pp. 228\u2013249, 2015.","DOI":"10.1016\/j.knosys.2015.07.006"},{"key":"2022081707553240065_j_comp-2020-0222_ref_030","doi-asserted-by":"crossref","unstructured":"R. G. Thippa and N. Khare, \u201cHybrid firefly-bat optimized fuzzy artificial neural network based classifier for diabetes diagnosis,\u201d Int. J. Intell. Eng. Syst., vol. 10, pp. 18\u201327, 2017.","DOI":"10.22266\/ijies2017.0831.03"},{"key":"2022081707553240065_j_comp-2020-0222_ref_031","doi-asserted-by":"crossref","unstructured":"C. Iwendi, P. K. R. Maddikunta, G. T. Reddy, L. Kuruva, B. K. Ali, and M. P. Jalil, \u201cA metaheuristic optimization approach for energy efficiency in the IoT networks,\u201d Softw: Pract Exper,  vol. 51, pp. 1\u201314, 2020.","DOI":"10.1002\/spe.2797"},{"key":"2022081707553240065_j_comp-2020-0222_ref_032","doi-asserted-by":"crossref","unstructured":"C. A. Jake, C. S. Long, P. S. Beth, T. L. Smith, and L. D. George, \u201cCombining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes,\u201d Expert Syst. Appl., vol. 115, pp. 245\u2013255, 2019.","DOI":"10.1016\/j.eswa.2018.08.002"},{"key":"2022081707553240065_j_comp-2020-0222_ref_033","doi-asserted-by":"crossref","unstructured":"X. Jia, B. M. Mirja, M. Farhaan, and G. H. Hamid, \u201cA cox-based risk prediction model for early detection of cardiovascular disease: Identification of key risk factors for the development of a 10-year CVD risk prediction,\u201d Adv. Preventive Med., vol. 2019, pp. 1\u201311, 2019.","DOI":"10.1155\/2019\/8392348"},{"key":"2022081707553240065_j_comp-2020-0222_ref_034","doi-asserted-by":"crossref","unstructured":"B. M. Donovan, P. J. Breheny, J. G. Robinson, R. J. Baer, A. F. Saftlas, W. Bao, et al., \u201cDevelopment and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women,\u201d PLoS ONE, vol. 14, pp. 1\u201321, 2019.","DOI":"10.1371\/journal.pone.0215173"},{"key":"2022081707553240065_j_comp-2020-0222_ref_035","doi-asserted-by":"crossref","unstructured":"Q. Wang, C. Weijia, J. Guo, J. Ren, C. Yongqiang, and D. N. Davis, \u201cDMP-MI: An effective diabetes mellitus classification algorithm on imbalanced data with missing values,\u201d IEEE Access, vol. 7, pp. 102232\u2013102238, 2019.","DOI":"10.1109\/ACCESS.2019.2929866"}],"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2020-0222\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2020-0222\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T08:00:54Z","timestamp":1660723254000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2020-0222\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3,16]]},"published-print":{"date-parts":[[2022,3,16]]}},"alternative-id":["10.1515\/comp-2020-0222"],"URL":"https:\/\/doi.org\/10.1515\/comp-2020-0222","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]}}}