Abstract
Abstract 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 Naïve 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.
Highlights
Several classification techniques of machine learning (ML) algorithms were discussed, and these techniques greatly helped the stakeholders of the medical field for predicting heart disease
A model of the artificial neural network (ANN) was proposed by Dangare and Apte was outperformed with 100% accuracy [1]
The implementation of ML algorithms was performed on a diabetic retinopathy dataset retrieved from the UCI ML repository
Summary
Several classification techniques of machine learning (ML) algorithms were discussed, and these techniques greatly helped the stakeholders of the medical field for predicting heart disease. To detect anomalies in hyperglycemia classification, techniques such as feedforward ANN, deep belief network, genetic algorithm (GA), support vector machine (SVM), and Bayesian neural network were proposed and implemented [2]. The combined models of ML and deep learning (DL) techniques were discussed to enhance accuracy for diabetes prediction [3]. The proposed system detected the exudates with an accuracy of 98.58% that was greater than other existing techniques [6]. The classification techniques such as C4.5, Naïve Bayes (NB), and clustering technique k-means clustering were used to detect the risk factors of diabetes disease complications. The proposed model achieved 95% accuracy for the two-class classification of the dataset size 30,000 images and 85% for the
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