Genetic Clustered Federated Learning for COVID-19 Detection

Kandati, Dasaradharami Reddy; Gadekallu, Thippa Reddy

doi:10.3390/electronics11172714

Cited by 25 publications

(15 citation statements)

References 39 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The challenges with the reliability of healthcare data and the constraints of predictive modelling are causing data mining efforts to fail. As Big Data has gained interest in recent years, involvement in the implementation of data mining techniques and methods to analyze healthcare data has emerged [28][29][30][31].…”

Section: Challenges Of Data Mining In Healthcarementioning

confidence: 99%

Data mining in predicting liver patients using classification model

2022

View full text Add to dashboard Cite

PurposeThis study proposes to identify potential liver patients based on the results of a liver function test performed during a health screening to search for signs of liver disease. It is critical to detect a liver patient at an early stage in order to treat them effectively. A liver function test's level of specific enzymes and proteins in the blood is evaluated to determine if a patient has liver disease. Methods According to a review of the literature, general practitioners (GPs) rarely investigate any anomalies in liver function tests to the level indicated by national standards. The authors have used data pre-processing in this work. The collection has 30691 records with 11 attributes. The classification model is utilized to construct an effective prediction system to aid general practitioners in identifying a liver patient using data mining. ResultsThe collected results indicate that both the Naïve Bayes and C4.5 Decision Tree models give accurate predictions. However, given the C4.5 model offers more accurate predictions than the Naïve Bayes model, it can be assumed that the C4.5 model is superior for this research. Consequently, the liver patient prediction system will be developed using the rules given by the C4.5 Decision Tree model in order to predict the patient class. The training set, suggested data mining with a classification model achieved 99.36% accuracy and on the testing set, 98.40% accuracy. On the training set, the enhanced accuracy relative to the current system was 29.5, while on the test set, it was 28.73. In compared to state-of-the-art models, the proposed approach yields satisfactory outcomes. ConclusionThe proposed technique offers a variety of data visualization and user interface options, and this type of platform can be used as an early diagnosis tool for liver-related disorders in the healthcare sector. This study suggests a machine learning-based technique for predicting liver disease. The framework includes a user interface via which healthcare providers can enter patient information.

show abstract

Section: Challenges Of Data Mining In Healthcarementioning

confidence: 99%

Data mining in predicting liver patients using classification model

2022

View full text Add to dashboard Cite

show abstract

“…A non-knowledge-based optimization process is used to evaluate the fitness function. Finding the global optimum and avoiding becoming trapped in the local optimum is simple 27 . A suite of potential solutions can be returned by multi-objective optimization.…”

Section: Predicting Software Maintainabilitymentioning

confidence: 99%

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Yenduri

Gadekallu

2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it’s important to assess software maintainability. Many software maintainability prediction models’ compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms.

show abstract

“…Researchers have identified the coronavirus (COVID-19) pandemic as one of the most devastating health crises now impacting millions of people worldwide [ 1 , 2 , 3 ]. When an infected person coughs, sneezes, or talks, respiratory particles are released into the air that can easily spread the COVID-19 virus.…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, Google initially presented the idea of FL as a fresh approach to ML. FL enables the building of global models without compromising the confidentiality of the underlying data [ 1 , 2 ]. During each training cycle, a subset of clients is selected to train a model with the data that are specific to their location.…”

Section: Introductionmentioning

confidence: 99%

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

Malik

Naeem

Loh

2023

Sensors

View full text Add to dashboard Cite

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients’ right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model’s accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

show abstract

Genetic Clustered Federated Learning for COVID-19 Detection

Cited by 25 publications

References 39 publications

Data mining in predicting liver patients using classification model

Data mining in predicting liver patients using classification model

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

Contact Info

Product

Resources

About