Feature Selection of Diabetic Retinopathy Disease Using Particle Swarm Optimization and Neural Network

Herliana, Asti; Arifin, Toni; Susanti, Sari; Hikmah, Agung Baitul

doi:10.1109/citsm.2018.8674295

Cited by 53 publications

(15 citation statements)

References 5 publications

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“…Additionally, the proposed algorithm is used to train feedforward multilayered perceptron ANNs. In [ 36 ], PSO was used for feature engineering of diabetic retinopathy, and after it, the NN classifier was applied for the classification of diabetic retinopathy disease.…”

Section: Related Workmentioning

confidence: 99%

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Bangyal

Hameed

Alosaimi

et al. 2021

Computational Intelligence and Neuroscience

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Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.

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Section: Related Workmentioning

confidence: 99%

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Bangyal

Hameed

Alosaimi

et al. 2021

Computational Intelligence and Neuroscience

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“…Neural Network merupakan salah satu dari sekian banyak algoritma data mining yang bisa digunakan untuk membuat prediksi data medis [10]. Neural Network adalah suatu konsep rekayasa pengetahuan dalam bidang kecerdasan buatan yang dirancang seperti syaraf pada manusia [11], seperti proses informasi pada otak manusia yang menghubungkan satu neuron ke neuron yang lain [12]. Neural Network sering disebut juga dengan Jaringan Syaraf Tiruan (JTS).…”

Section: Pendahuluanunclassified

“…Penerapan metode Neural Network dan Particle Swarm Optimization pada Caesarian Section Classification Dataset bertujuan guna mendapatkan akurasi yang diharapkan bisa meningkatkan akurasi pada klasifikasi pasien caesar dari penelitian-penelitian yang telah dilakukan [17], [3], dan [12]. Pada eksperimen akan terlihat hasil akurasi, baik itu akurasi yang optimal ataupun tidak.…”

Section: Hasil Dan Pembahasanunclassified

Penentuan Penanganan Persalinan Caesar dengan Neural Network dan Particle Swarm Optimization

Setia¹,

Arifin²

2021

SISTEMASI

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Persalinan caesar merupakan suatu tindakan operatif pada ibu bersalin dengan melakukan insisi pada kulit, dinding perut dan dinding rahim untuk menyelamatkan ibu serta bayi. Tindakan caesar dilakukan jika sang ibu tidak dapat melahirkan secara normal yang disebabkan oleh suatu indikasi tertentu. Untuk mengurangi resiko persalinan pada ibu dan bayi, perlu dilakukan pemeriksaan kondisi calon pasien caesar, maka dapat dilakukan penerapan dan pemanfaatan teknik data mining guna meminimalisir kesalahan dalam pemeriksaan. Klasifikasi yaitu salah satu poin penting dalam data mining atau pembelajaran mesin. Klasifikasi merupakan suatu pengelompokan data dimana data tersebut memiliki kelas label atau target. Salah satu metode data mining untuk masalah klasifikasi yang bisa implementasikan yaitu Neural Network. Untuk meningkatkan hasil akurasi penelitian, maka dapat digunakan pembobotan atribut menggunakan Particle Swarm Optimization. Pada penelitian yang dilakukan, teknik klasifikasi metode Neural Network dan Particle Swarm Optimization diterapkan pada Caesarian Section Classification Dataset. Setelah penelitian selesai dilakukan, diperoleh hasil akurasi menggunakan Neural Network mencapai 87.50% dengan nilai Area Under Curve (AUC) yaitu 1.000. Kemudian hasil akurasi menggunakan Neural Network berbasis Particle Swarm Optimization mengalami peningkatan sebesar 6.25% dengan akurasi mencapai 93.75% dan Area Under Curve (AUC) yaitu 0.913.

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“…Metode DT menghasilkan akurasi lebih tinggi dari metode lain untuk data kelulusan mahasiswa [17], klasifikasi penyakit kutil [18], dan klasifikasi mahasiswa baru [19]. Hasil dari metode klasifikasi dapat ditingkatkan akurasinya dengan menggunakan metode Particle Swarm Optimization (PSO) seperti dalam [20]. Secara umum, karakteristik metode ini mempunyai konsep sederhana, komputasi yang efisien, dan implementasi yang mudah [21].…”

Section: Pendahuluanunclassified

Optimizing decision tree using particle swarm optimization to identify eye diseases based on texture analysis

Arifin¹,

Herliana²

2019

Jurnal Teknologi dan Sistem Komputer

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The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.

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Feature Selection of Diabetic Retinopathy Disease Using Particle Swarm Optimization and Neural Network

Cited by 53 publications

References 5 publications

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Penentuan Penanganan Persalinan Caesar dengan Neural Network dan Particle Swarm Optimization

Optimizing decision tree using particle swarm optimization to identify eye diseases based on texture analysis

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