Acute sinusitis data classification using grey wolf optimization-based support vector machine

Putri, Ajeng Maharani; Rustam, Zuherman; Pandelaki, Jacub; Wirasati, Ilsya; Hartini, Sri

doi:10.11591/ijai.v10.i2.pp438-445

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Because of its excellent accuracy and ability to manage data with large dimensions, the SVM approach is widely employed in bioinformatics. [25] SVM seeks to optimize the margin by finding a hyper-plane between two specific categories in the data. [26] The hyper-plane linear model is represented by equation ( 8): [27]…”

Section: Svmmentioning

confidence: 99%

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Sharma¹,

Kaur²

2022

APJHS

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The massive generation of medical data from smart health-care applications in recent years necessitates the development of big data classification strategies. Medical data classification can be used to visualize patterns in the data and detect the presence of the disease in medical data. We present an efficient support vector machine (SVM) hybridized with a grey wolf optimization (GWO) algorithm for chronic kidney disease (CKD) data classification in this work. Initially, infinite feature selection (IFS) algorithm is used to select the best features from a set of available features. The dataset’s selected features are processed and fed into the GWO optimized SVM algorithm. The proposed CKD classification strategy has been simulated in MATLAB. CKD dataset from UCI machine learning repository is utilized for testing the developed strategy. The performance of the proposed CKD classification strategy is examined by accuracy and root mean square error (RMSE) values. According to the investigational findings, the proposed CKD classification system achieved accuracy and RMSE value of 97.58% and 0.1581, respectively, for classifying subjects into the CKD and non-CKD categories. The performance of GWO optimized SVM algorithm is outstanding, according to the experimental observations.

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Section: Svmmentioning

confidence: 99%

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Sharma¹,

Kaur²

2022

APJHS

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“…As an optimization algorithm grey wolf optimization (GWO) has been used for different applications domains due to its simplicity to adapt in any optimization algorithm [38]- [43]. It successfully showed promising results such as in wireless sensor [44], resource allocation in cloud environment [45], classification [45], feature selection [46], and other optimization problems [47], [48].…”

Section: Introductionmentioning

confidence: 99%

Grey wolf optimization algorithm for hierarchical document clustering

Jabbar

Ku-Mahamud²

2021

IJEECS

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).

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Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

Bulfamante

Ferella

Miller

et al. 2022

Eur Arch Otorhinolaryngol

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Purpose This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. Methods MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability. Results Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%. Conclusions AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.

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Acute sinusitis data classification using grey wolf optimization-based support vector machine

Cited by 3 publications

References 21 publications

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Grey wolf optimization algorithm for hierarchical document clustering

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

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