An optimal multi-disease prediction framework using hybrid machine learning techniques

Gupta, Aditya K.; Singh, Amritpal

doi:10.48129/kjs.splml.19321

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The proposed ensemble method is also compared with the existing ensemble methods such as AB-WAE (9) , Stratified K fold (10) , GAE-RFE (12) , soft voting classifier (13) models and SEIML (17) . Table 2 shows the detailed data.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Heart Disease using Ensemble Learning

Muthulakshmi¹,

Parveen²,

Rajeswari³

2023

IJST

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Objectives: To propose a Bagging ensemble method to predict heart disease at early stages. The main focus of this research is to increase the prediction accuracy in a model. Methods: The proposed system is experimented with by using the Cleveland datasets collected from the UCI repository. The dataset consists of 14 attributes. In this dataset we applied different machine learning algorithms such as Decision tree, Naïve Bayes, Random Forest and SVM along with the proposed ensemble learning classifier. The entire dataset is trained upon the Pearson correlation coefficient selected features under the k-fold cross-validation setup. Final outcome is obtained by aggregating the prediction accuracy. Findings: The performance of the proposed method was validated using prediction accuracy and compared with the Machine learning algorithms and the ensemble models. The proposed method attains a higher classification accuracy of 95.33% than all other methods. Novelty: A novel ensemble method has been proposed with a better accuracy in early predicting of the heart disease.

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Section: Resultsmentioning

confidence: 99%

“…Adithya et al (12) formed an optimal multi-disease prediction framework using hybrid machine learning techniques. They have used genetic algorithm-based recursive feature elimination and the AdaBoost (GAE-RFE) method to predict the disease.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Heart Disease using Ensemble Learning

Muthulakshmi¹,

Parveen²,

Rajeswari³

2023

IJST

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“…Becauseoftheresourceconstraintsandbattery-powerednatureofIoTandmobiledevices,theproduced burdenisoffloadedtomorepowerfulfog/cloudserversintheuppertiertomeettherequirementsof delay-sensitiveapplicationslikehealthcare,military,andautomotivenetworks,amongothers(A. Gupta &Singh,2022a,2022b.Dependinguponthealgorithmappliedtotaskoffloading,thesingleormultidevicecanhandlethetransmittedworkload.Multipledevicessupportparallelprocessingofincoming…”

Section: Task Offloadingmentioning

confidence: 99%

Quality of Service-Based Resource Management in Fog Computing

Jain

Kumar

2022

International Journal of Cloud Applications and Computing

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In recent years, the emergence of the internet of things (IoT) has accelerated the quality of day-to-day tasks. With the rapid development of IoT devices, the cloud computing paradigm has become an attractive solution by facilitating on-demand services. However, the remote location of third-party cloud reduces the overall user experience while increasing the latency involved. Fog computing has been introduced as a promising solution by improving the overall service quality. Fog computing comprises distributed and heterogeneous fog nodes with limited resource capabilities. Therefore, managing fog resources while satisfying users' service quality is a challenging task. This study attempts to conduct a systematic review by examining high-quality research papers published between 2018 and April 2022. This paper aims to address current trends, challenges, and theoretical gaps with a wide range of open issues to guide the researchers and practitioners interested in carrying out the further research in the current domain.

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Exploring Sleep Disorder and Lifestyle Analysis Through Data Preprocessing and Ensemble Learning Techniques

Sravani,

et al. 2024

2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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An optimal multi-disease prediction framework using hybrid machine learning techniques

Cited by 4 publications

References 15 publications

Prediction of Heart Disease using Ensemble Learning

Prediction of Heart Disease using Ensemble Learning

Quality of Service-Based Resource Management in Fog Computing

Exploring Sleep Disorder and Lifestyle Analysis Through Data Preprocessing and Ensemble Learning Techniques

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