Retraction Note to: Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system

Harimoorthy, Karthikeyan; Thangavelu, Menakadevi

doi:10.1007/s12652-022-03971-1

Cited by 9 publications

(8 citation statements)

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“…After FS, out of 13 features, only 7 features were selected and then SVM was used for classification. Recently, Harimoorthy and Thangavelu [81] selected the features using Chi-square based filter method. However, their major contribution was that they improved the radial basis kernel of the SVM model where they iteratively decreased the margin size i.e., increased the cost during model training.…”

Section: Single Classifier Based Methodsmentioning

confidence: 99%

“…However, oversampling leads to the synthetic generation of data which is not entirely reliable for sensitive domains like disease prediction. To deal with the missing values, Harimoorthy and Thangavelu [81] removed missing values by ignoring the missing fields. After using the X-square filter FS, the authors detected the presence of disease using the modified redial basis kernel of SVM.…”

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

“…This method is similar to the one used by Patil et al [160] in Diabetes detection and thus had the same problem of dataset shrinkage as the former. In another work, Harimoorthy and Thangavelu [81] modified the redial basis kernel (discussed earlier) of SVM which in turn decreased the classification error of the SVM model.…”

Section: Ckd Prediction Methodsmentioning

confidence: 99%

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Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Section: Single Classifier Based Methodsmentioning

confidence: 99%

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

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Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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“…In the early stage of the 30-year IDS history, the majority of systems are rule-based detection modes (Ayo et al, 2020). After rule-based systems, different algorithm-based detection methods, such as GA (Katoch et al, 2021), Bayesian (Marcot and Penman, 2019), neural networks (NN) (Yamashita et al, 2018) and SVM (Harimoorthy and Thangavelu, 2021) have been developed. proposed intrusion response systems (IRS) and intrusion risk assessment (IRA), both of which are important aspects of IDS.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

A novel dimension reduction based method to detect network intrusions

Chang

Nalluri

Chen

et al. 2022

Preprint

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There is a steep rise in the trend of the utility of internet technology nowadays. This tremendous increase ushered in a massive amount of data generated and personal information has been stored in the cloud. For apparent reasons, undivided attention could cause huge economic losses and cause serious security issues. Therefore, effective security solutions for detecting and combating cyber-attacks have become increasingly important. In addition, intrusion detection systems can be divided into several detection methods based on data type and analysis methods. However, no one method is absolutely effective in a variety of environments. For different types of cyber-attacks, various intrusion detection systems have been developed using artificial intelligence or machine learning techniques, but with the change in attack patterns, the intrusion detection performance of these systems needs to be greatly improved. In this study, the anomaly detection concept is used to propose a new local kernel-principal component analysis (LKPCA) method to improve classification performance. In addition, we are developing two multi-class learning strategies for binary classifiers to build multi-class classifiers. The analysis was duly conducted on the benchmark NSL-KDD and KDD datasets. The proposed method is validated using cross-validation and compared with existing feature extraction methods. This method outperformed over the existing intrusion detection methods used for the dataset.

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“…However, the classifier showed an accuracy rate of 84.36% after feature selection. Thus the significance of feature selection as well as extraction in improving the performance of the trained model in predicting the disease was revealed [19,20].…”

Section: Introductionmentioning

confidence: 99%

Predicting Heart Disease Using FTGM-PCA Based Informative Entropy Based-Random Forest

Deenathayalan

Narayanan²

2022

Curr. Appl. Sci. Technol.

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In recent years, heart disease has become a reason for high mortality rate, and data mining has also gained attention in the medical domain. Predicting this disease in its initial stage helps to save lives and reduce treatment costs. Various classification models were recently introduced with expected outcomes. However, they lacked prediction accuracy. Hence, the aim of this study was to employ data mining techniques for predicting heart disease, and focused on higher accuracy. This disease was predicted by considering the Cleveland heart disease dataset, employing deep CNN models for extracting relevant features, and performing feature level fusion related to its efficient and automatic learning. FGM-PCA (Fast Track Gram Matrix-Principal Component Analysis) was proposed for dimensionality reduction and fusion to solve overfitting issues, minimise time and space complexity, eliminate redundant data, and enhance classifier performance. Further, effective classification was achieved through the newly introduced IEB-RF (Informative Entropy Based-Random Forest) because it offers high accuracy and can also handle a large amount of data flexibly. The proposed system was evaluated in terms of accuracy, sensitivity, F1-score, AUC (Area under Curve) and precision. The results revealed the superior performance of the introduced system in comparison to traditional techniques.

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Retraction Note to: Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system

Cited by 9 publications

References 0 publications

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

A novel dimension reduction based method to detect network intrusions

Predicting Heart Disease Using FTGM-PCA Based Informative Entropy Based-Random Forest

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