Application of Machine Learning to the Detection of Retinal Diseases
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Keywords

Decision Tree
Logistic Regression

How to Cite

Regin, R., Rajest, S. S., T, S., & R, S. (2024). Application of Machine Learning to the Detection of Retinal Diseases. European Journal of Life Safety and Stability (2660-9630), 37, 1-23. Retrieved from http://ejlss.indexedresearch.org/index.php/ejlss/article/view/1141

Abstract

In this study, we use a machine learning method to identify cases of diabetic retinopathy in humans. The suggested approach uses classification algorithms on various variables from an existing Diabetic Retinopathy dataset, such as optical disc diameter, lesion-specific information (microaneurysms, exudates, or presence of haemorrhages), and so on. Following feature extraction, the presence of diabetic retinopathy may be predicted. A Decision Tree, a Logistic Regression, and a Support Vector Machine were all utilised in the proposed system's prediction process. Results from the current works were significantly worse than the suggested method's 88% accuracy. Using the SVM method, it can detect the existence of diabetic retinopathy, macular degeneration, myopia, and other retinal illnesses. The next step is to sort them according to their hue and morphological assumptions. For improved accuracy, the system is classified using an approach that combines Decision Trees with Logistic Regression and Support Vector Machines.

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V. Dankan Gowda, K. Prasad, R. Shekhar, R. Srinivas, K. N. V. Srinivas and P. K. Lakineni, "Development of a Real-time Location Monitoring App with Emergency Alert Features for Android Devices," 2023 4th IEEE Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2023, pp. 1-8.

Karimisetty, S., Kumar Lakineni, P., Kamalanjali, M.L., Parwekar, P. (2021). Automated Water Flow Control System in Overhead Tanks Using Internet of Things and Mobile Application. In: Mahapatra, R.P., Panigrahi, B.K., Kaushik, B.K., Roy, S. (eds) Proceedings of 6th International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 177. Springer, Singapore.

C. S. Rao, A. Balakrishna, K. B. Raju, L. P. Kumar, S. V. Raju and G. V. P. Raju, "Implementation of Interactive Data Knowledge Management and Semantic Web for Step-Data from Express Entities," 2010 3rd International Conference on Emerging Trends in Engineering and Technology, Goa, India, 2010, pp. 537-542.

K. B. Raju, C. S. Rao, L. PrasannaKumar and S. V. Raju, "PDM data classification from step - An object oriented string matching approach," 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, 2011, pp. 1-9.

D. R. Giri, S. P. Kumar, L. Prasannakumar and R. N. V. V. Murthy, "Object oriented approach to SQL injection preventer," 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12), Coimbatore, India, 2012, pp. 1-7.

K. Butchi Raju, P. Kumar Lakineni, K. S. Indrani, G. Mary Swarna Latha and K. Saikumar, "Optimized building of machine learning technique for thyroid monitoring and analysis," 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2021, pp. 1-6.

R. Rai, V. Y. Nguyen, and J. H. Kim, “Variability analysis and evaluation for major cut flower traits of F1 hybrids in Lilium brownii var. colchesteri,” Journal of multidisciplinary sciences (Online), vol. 4, no. 2, pp. 35–41, Dec. 2022.

V. Y. Nguyen, R. Rai, J.-H. Kim, J. Kim, and J-K Na, “Ecogeographical variations of the vegetative and floral traits of Lilium amabile Palibian,” Journal of Plant Biotechnology, vol. 48, no. 4, pp. 236–245, Dec. 2021.

R. Rai and J.H. Kim, “Performance Evaluation And Variability Analysis For Major Growth And Flowering Traits Of Lilium longiflorum Thunb. Genotypes,” Journal of Experimental Biology and Agricultural Sciences, vol. 9, no. 4, pp. 439–444, Aug. 2021.

R. Rai, V. Y. Nguyen, and J. H. Kim, “Estimation Of Variability Analysis Parameters For Major Growth And Flowering Traits Of Lilium leichtlinii var. maximowiczii GERMPLASM,” Journal of Experimental Biology and Agricultural Sciences, vol. 9, no. 4, pp. 457–463, Aug. 2021.

R. Rai and J. H. Kim, “Effect Of Storage Temperature And Cultivars On Seed Germination OF Lilium×formolongi HORT.,” Journal of Experimental Biology and Agricultural Sciences, vol. 8, no. 5, pp. 621–627, Oct. 2020.

R Rai, &J.H. Kim ,Estimation of combining ability and gene action for growth and flowering traits in Lilium longiflorum.International Journal of Advanced Science and Technology,Vol.29 No.8S pp 1356-1363,2020

R. Rai, A. Badarch, and J.-H. Kim, “Identification Of Superior Three Way-Cross F1s, Its Line×Tester Hybrids And Donors For Major Quantitative Traits In Lilium×formolongi,” Journal of Experimental Biology and Agricultural Sciences, vol. 8, no. 2, pp. 157–165, Apr. 2020.

R. Rai, J. Shrestha, and J. Kim, “Line×tester analysis in lilium×formolongi: identification of superior parents for growth and flowering traits,” SAARC Journal of Agriculture, vol. 17, no. 1, pp. 175–187, Aug. 2019.

R. Rai,J.Shrestha and J.H.Kim “Combining Ability and Gene Action Analysis of Quantitative Traits in Lilium × formolongi,” vol. 30, no. 3, pp. 131–143, Dec. 2018.

T. X. Nguyen, S.-I. Lee, R. Rai, N. Kim, and Jong Hwa Kim, “Ribosomal DNA locus variation and REMAP analysis of the diploid and triploid complexes ofLilium lancifolium,” Genome, vol. 59, no. 8, pp. 551–564, Aug. 2016.

N. X. Truong, J. Y. Kim, R. Rai, J. H. Kim, N. S. Kim, and A. Wakana, “Karyotype Analysis of Korean Lilium maximowiczii Regal Populations,” Journal of The Faculty of Agriculture Kyushu University, vol. 60, no. 2, pp. 315–322, Sep. 2015.

Gaurav Kumawat, Santosh Kumar Viswakarma, Prasun Chakrabarti , Pankaj Chittora, Tulika Chakrabarti , Jerry Chun-Wei Lin, “Prognosis of Cervical Cancer Disease by Applying Machine Learning Techniques”, Journal of Circuits, Systems, and Computers, 2022.

Akhilesh Kumar Sharma, Gaurav Aggarwal, Sachit Bhardwaj, Prasun Chakrabarti, Tulika Chakrabarti, Jemal Hussain, Siddhartha Bhattarcharyya, Richa Mishra, Anirban Das, Hairulnizam Mahdin, “Classification of Indian Classical Music with Time-Series Matching using Deep Learning”, IEEE Access , 9 : 102041-102052 , 2021.

Akhilesh Kumar Sharma, Shamik Tiwari, Gaurav Aggarwal, Nitika Goenka, Anil Kumar, Prasun Chakrabarti, Tulika Chakrabarti, Radomir Gono, Zbigniew Leonowicz, Michal Jasiński , “Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network”, IEEE Access , 10 : 17920-17932, 2022.

Abrar Ahmed Chhipa , Vinod Kumar, R. R. Joshi, Prasun Chakrabarti, Michal Jaisinski, Alessandro Burgio, Zbigniew Leonowicz, Elzbieta Jasinska, Rajkumar Soni, Tulika Chakrabarti, “Adaptive Neuro-fuzzy Inference System Based Maximum Power Tracking Controller for Variable Speed WECS”, Energies ,14(19) :6275, 2021.

Chakrabarti P. , Goswami P.S., “Approach towards realizing resource mining and secured information transfer”, International Journal of Computer Science and Network Security, 8(7), pp.345-350, 2008.

Chakrabarti P., Choudhury A., Naik N. , Bhunia C.T., “Key generation in the light of mining and fuzzy rule”, International Journal of Computer Science and Network Security, 8(9), pp.332-337, 2008.

Chakrabarti P., De S.K., Sikdar S.C., “Statistical Quantification of Gain Analysis in Strategic Management” , International Journal of Computer Science and Network Security,9(11), pp.315-318, 2009.

Chakrabarti P. , Basu J.K. , Kim T.H., “Business Planning in the light of Neuro-fuzzy and Predictive Forecasting”, Communications in Computer and Information Science , 123, pp.283-290, 2010.

Prasad A. , Chakrabarti P., “Extending Access Management to maintain audit logs in cloud computing", International Journal of Advanced Computer Science and Applications ,5(3),pp.144-147, 2014.

Sharma A.K., Panwar A., Chakrabarti P. ,Viswakarma S., “Categorization of ICMR Using Feature Extraction Strategy and MIR with Ensemble Learning”, Procedia Computer Science, 57,pp.686-694,2015.

Patidar H. , Chakrabarti P., “A Novel Edge Cover based Graph Coloring Algorithm”, International Journal of Advanced Computer Science and Applications , 8(5),pp.279-286,2017.

Patidar H., Chakrabarti P., Ghosh A., “Parallel Computing Aspects in Improved Edge Cover based Graph Coloring Algorithm”, Indian Journal of Science and Technology ,10(25),pp.1-9,2017.

Tiwari M., Chakrabarti P, Chakrabarti T., “Novel work of diagnosis in liver cancer using Tree classifier on liver cancer dataset ( BUPA liver disorder )” , Communications in Computer and Information Science , 837, pp.155-160, 2018.

Verma K., Srivastava P. , Chakrabarti P., “Exploring structure oriented feature tag weighting algorithm for web documents identification”, Communications in Computer and Information Science ,837, pp.169-180, 2018.

Tiwari M., Chakrabarti P , Chakrabarti T., “Performance analysis and error evaluation towards the liver cancer diagnosis using lazy classifiers for ILPD”, Communications in Computer and Information Science , 837, pp.161-168,2018.

Patidar H. , Chakrabarti P., “A Tree-based Graphs Coloring Algorithm Using Independent Set”, Advances in Intelligent Systems and Computing, 714, pp. 537-546, 2019.

Chakrabarti P., Satpathy B., Bane S., Chakrabarti T., Chaudhuri N.S. , Siano P., “Business forecasting in the light of statistical approaches and machine learning classifiers”, Communications in Computer and Information Science , 1045, pp.13-21, 2019.

S. Mandvikar, “Factors to Consider When Selecting a Large Language Model: A Comparative Analysis,” International Journal of Intelligent Automation and Computing, vol. 6, no. 3, pp. 37–40, 2023.

S. Mandvikar, “Augmenting intelligent document processing (IDP) workflows with contemporary large language models (LLMs),” International Journal of Computer Trends and Technology, vol. 71, no. 10, pp. 80–91, 2023.

R. Boina, A. Achanta, and S. Mandvikar, “Integrating data engineering with intelligent process automation for business efficiency,” International Journal of Science and Research, vol. 12, no. 11, pp. 1736–1740, 2023.

S. Mandvikar and A. Achanta, “Process automation 2.0 with generative AI framework,” Int. J. Sci. Res. (Raipur), vol. 12, no. 10, pp. 1614–1619, 2023.

Mandvikar, S. (2023). Indexing robotic process automation products. International Journal of Computer Trends and Technology, 71(8), 52–56.

M. A. Veronin, R. P. Schumaker, R. R. Dixit, and H. Elath, ‘Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic’, Drug, Healthcare and Patient Safety, pp. 65–70, 2019.

M. A. Veronin, R. P. Schumaker, and R. Dixit, ‘The irony of MedWatch and the FAERS database: an assessment of data input errors and potential consequences’, Journal of Pharmacy Technology, vol. 36, no. 4, pp. 164–167, 2020.

M. A. Veronin, R. P. Schumaker, R. R. Dixit, P. Dhake, and M. Ogwo, ‘A systematic approach to’cleaning’of drug name records data in the FAERS database: a case report’, International Journal of Big Data Management, vol. 1, no. 2, pp. 105–118, 2020.

R. P. Schumaker, M. A. Veronin, T. Rohm, M. Boyett, and R. R. Dixit, ‘A Data Driven Approach to Profile Potential SARS-CoV-2 Drug Interactions Using TylerADE’, Journal of International Technology and Information Management, vol. 30, no. 3, pp. 108–142, 2021.

R. Schumaker, M. Veronin, T. Rohm, R. Dixit, S. Aljawarneh, and J. Lara, ‘An Analysis of Covid-19 Vaccine Allergic Reactions’, Journal of International Technology and Information Management, vol. 30, no. 4, pp. 24–40, 2021.

M. A. Veronin, R. P. Schumaker, R. R. Dixit, and H. Elath, ‘Opioids and Frequency Counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) Database’, Current Aspects in Pharmaceutical Research and Development Vol. 8, pp. 35–43, 2022.

R. P. Schumaker, M. A. Veronin, and R. R. Dixit, ‘Determining Mortality Likelihood of Opioid Drug Combinations using Decision Tree Analysis’, 2022.

M. A. Veronin, R. P. Schumaker, R. Dixit, and M. Ogwo, ‘Irony of the FAERS Database: An Analysis of Data Input Errors and Potential Consequences’, in IIMA/ICITED Joint Conference 2018, 2018, pp. 101–116.

R. Schumaker, M. Veronin, R. Dixit, P. Dhake, and D. Manson, ‘Calculating a Severity Score of an Adverse Drug Event Using Machine Learning on the FAERS Database’, in IIMA/ICITED UWS Joint Conference, 2017, pp. 20–30.

R. Dixit, R. P. Schumaker, and M. A. Veronin, ‘A Decision Tree Analysis of Opioid and Prescription Drug Interactions Leading to Death Using the FAERS Database’, in IIMA/ICITED Joint Conference 2018, 2018, pp. 67–67.