Security-enhanced machine learning model for diagnosis of knee joint disorders using vibroarthrographic signals

Balajee, A.; Murugan, R.; Venkatesh, K.

doi:10.1007/s00500-023-07934-2

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Apart from feature selection, feature extraction mechanisms are also implemented directly towards the gene expression data by applying the deep learning models which demand the data with a large number of samples stated Vinoth Kumar et al [18]. Hybridization can be implemented to the feature selection mechanism which in turn could produce the better feature vector stated by Balajee et al [19]. V. Rupara et al [20] proposed a hybrid feature selection mechanism that integrates resampling and statistical tests to calculate the set of features that could lead to better prediction.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

Mahesh,

Santhakumar,

Balajee

et al. 2024

IEEE Access

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Leukemia refers to a type of blood malignancy that develops due to certain hematological disorders. Identifying leukemia at its earlier stages through clinical operations are highly complicated task with invasive methods. Gene expression data could be collected and computational methods could be adopted which could lead to better prediction of leukemia that leads to prevention at its earlier stages. Today, feature selection has become an important step in pre-processing that helps bring improvement to the classification system and its performance that is done by choosing optimal feature subsets by means of reducing or eliminating redundant or irrelevant features. Particle Swarm Optimization (PSO) is a popular algorithm wherein certain solutions that are generated randomly move within the search space to obtain optimal solutions. Another relatively new and evolutionary method computation is the Ant Lion Optimization (ALO) algorithm that has lower computation cost compared to the other techniques. In this work, a new technique known as the Hybrid Ant Lion Mutated Ant Colony Optimize along with Particle Swarm Optimization (PSO) was proposed for the prediction of leukaemia with the microarray gene data. The proposed model that is used for identifying the optimal set of features from which the classification has been done using the Support Vector Machine (SVM) has produced a significant prediction accuracy of 87.88%.

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Section: Related Workmentioning

confidence: 99%

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

Mahesh,

Santhakumar,

Balajee

et al. 2024

IEEE Access

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Kernel-Induced Matriarch Path Tracking Elephant Herding Optimization Technique for Identification and Classification of Cancer Types Using Support Vector Machines

Senbagamalar,

Logeswari

2023

Int. J. Patt. Recogn. Artif. Intell.

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Abnormal cells in the human body that keep on mutating are termed to be cancer in medical terms. There are multiple types of cancer identified in human beings. It is very much essential to identify and classify the type of cancer in its earlier stage. This objective can be satisfied by artificial intelligence which has a subfield of machine learning to create a generalized model that could identify and classify cancer with increased performance. To perform the identification and classification of various cancer types, in this paper, two techniques are adopted. The optimized feature set computation was done using the Kernel-Induced Matriarch path tracking Elephant Herding Optimization (KIM-EHO) and the classification for the given samples was done using the Support Vector Machines (SVM). The proposed techniques are implemented with the benchmark datasets and the results proved that the proposed methodologies outperformed the existing methods in terms of accuracy, specificity, sensitivity and time complexity.

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Application of Eemd-Dfa Algorithms and Ann Classification for Detection of Knee Osteoarthritis Using Vibroarthrography

MACHROWSKA,

KARPIŃSKI,

MACIEJEWSKI

et al. 2024

Appl. Comput. Sci.

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Osteoarthritis is one of the leading causes of disability around the globe. Up to this date there is no definite cure for cartilage lesions. Only fast and accurate diagnosis enables prolonging joint survivor time. Available diagnostic methods have disadvantages such as high price, radiation, need for experienced radiologists or low availability in some regions. The present study evaluates the use of vibroarthorgraphy as a method of cartilage lesion detection. 47 patients with diagnosed cartilage lesions, and 51 healthy control group patients have been enrolled in this study. The cartilage in the study group was evaluated intraoperatively by experienced orthopaedic surgeon. Signal acquisition was performed in open and closed kinematic chain based on 10 knee joint movements from 0-90 degrees. By using EEMD-DFA algorithms, reducing classifier inputs using ANOVA and then classifying using artificial neural networks (ANN), a classification accuracy of almost 93% was achieved. A sensitivity of 0.93 and a specificity of 0.93 with an AUC of 0.942 were obtained for the multilayer perceptron network. These results allow to apply this testing protocol in a clinical setting in the future.

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Security-enhanced machine learning model for diagnosis of knee joint disorders using vibroarthrographic signals

Cited by 3 publications

References 23 publications

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

Kernel-Induced Matriarch Path Tracking Elephant Herding Optimization Technique for Identification and Classification of Cancer Types Using Support Vector Machines

Application of Eemd-Dfa Algorithms and Ann Classification for Detection of Knee Osteoarthritis Using Vibroarthrography

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