Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

Sahu, Sanat Kumar; Shrivas, Akhilesh Kumar

doi:10.4018/ijcvip.2018100102

Cited by 3 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…FGCA is an improved fuzzy C-means clustering algorithm based on genetic optimization. First, a set of cluster centers close to the global optimal value is determined by GA, and then, the obtained cluster centers are used as the initial cluster centers of the FCM algorithm [20]. Since its value is close to the global optimal solution, the local search ability of the FCM algorithm itself becomes an advantage.…”

Section: Feature Extraction Image Segmentationmentioning

confidence: 99%

Image Processing Method Based on FGCA and Artificial Neural Network

Yuan

2022

Scientific Programming

View full text Add to dashboard Cite

With the aggravation of human sub-health problems, people’s demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people’s diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods.

show abstract

Section: Feature Extraction Image Segmentationmentioning

confidence: 99%

Image Processing Method Based on FGCA and Artificial Neural Network

Yuan

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Clustering dengan menggunakan algoritma k-mean memanfaatkan euclidean serta fungsi jarak manhattan dibandingkan untuk menganalisis data pasien PGK, dan peningkatan jumlah cluster untuk membangun modul prediksi yang baik dalam menentukan pasien PGK dan bukan pasien PGK [7]. Penggunaan teknik seleksi fitur pada basis data PGK dengan algoritma genetik pada tiga teknik clustering, yaitu: K-means, Fuzzy C-means dan Hierarchical clustering menghasilkan teknik clustering yang terbaik adalah menggunakan Hierarchical clustering [8].…”

Section: Implementasi Algoritma Dbscan Dalam Mengelompokan Data Pasie...unclassified

Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK)

Anggara¹,

Rahman²

2022

algoritme

View full text Add to dashboard Cite

Penyakit ginjal kronis(PGK) adalah penyakit ginjal yang ditandai dengan kerusakan struktural atau fungsional pada ginjal yang berlangsung lebih dari 3 bulan. PGK ditandai dengan satu atau lebih tanda kerusakan ginjal, yaitu albuminuria, sedimen urin abnormal, elektrolit, histologi, struktur ginjal, atau riwayat transplantasi ginjal, dengan penurunan laju filtrasi glomerulus. Pada penelitian ini dilakukan implementasi algoritma DBSCAN untuk mengelompokan data pasien tediagnosis PGK. Basis data yang digunakan adalah Chronic Kidney Disease yang berjumlah 400 data medis dengan 24 atribut atau fitur. Hasil penelitian dievaluasi dengan mencatat banyak jumlah cluster dan noise yang didapatkan menggunakan metric euclidean. Hasil terbaik ada pada skenario kedua dengan nilai epsilon sebesar 3,5, dan Min sample=5 yang menghasilkan jumlah cluster sebanyak 2 cluster dengan nilai Silhouette sebesar 0.158.

show abstract

“…The proposed SVM and KNN methods are tested and the accuracy of both the approaches are recorded as 71.52% and 94.74%, respectively. The essential idea of the Genetic Algorithm is utilized to generate solutions and to determine improvement issues [17]. Zahoor and Zafar [18] have discussed the microarray technology that produces thousands of genes in a single study or record.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce features from the dataset, we also used Principal Component Analysis (PCA) [21] for dimensional reduction, and feature selection techniques [22] to reduce the features from the original feature space. Genetic-Algorithms (GA) [17,23] were utilized to the order of development to determine advancement issues. The most cited presentation of the one of a kind Genetic algorithmic standard was developed by John Holland who described it in the mid-1970s [24] and hereditary calculations is versatile inquiry procedures that bolstered the standards of a normal activity in science.…”

Section: Feature Selectionmentioning

confidence: 99%

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Chandrakar¹,

Shrivas²,

Sahu³

2021

EAI Endorsed Transactions on Pervasive Health and Technology

View full text Add to dashboard Cite

INTRODUCTION: Gene expression levels are important for identifying and diagnosing diseases like cancer. Gene expression microarray information contains a high extent feature set, which minimizes the performance and the accuracy of classifiers. OBJECTIVES: This paper proposes a Modified Genetic Algorithm (MGA) that is based on Classifier Subset Evaluators -Genetic Search (Eval-CSE_GS) for selecting the relevant feature subsets. The MGA feature selection procedure is applied to microarray information for cancer patients that minimize a high dimension feature subset into low dimension feature subsets. METHODS: The various data mining methods for classifying the various kinds of cancer disease patients are presented. The proposed model refers to an ensemble model (PEM) for the organization of cancer disease by reducing the feature subsets, which results show improvements in the success rate. RESULTS: The proposed ensemble model obtains the accuracy of 94.58%, 96.56% and 97.04% for PEM-1 to PEM-3, respectively. CONCLUSION: Our proposed MGA-PEM model gives satisfactory results for cancer identification and classification.

show abstract

Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

Cited by 3 publications

References 16 publications

Image Processing Method Based on FGCA and Artificial Neural Network

Image Processing Method Based on FGCA and Artificial Neural Network

Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK)

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Contact Info

Product

Resources

About