Development and evaluation of the bootstrap resampling technique based statistical prediction model for Covid-19 real time data : A data driven approach

Karthick, K.; Aruna, S.; Manikandan, R.

doi:10.1080/09720502.2021.2012890

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Because of the increased collection of medical data, practitioners now have a great opportunity to promote healthcare diagnosis. ML plays a vital role in many applications like text detection and recognition [ 3 ], early prediction [ 4 ], power quality disturbance detection [ 5 ], truck traffic classification [ 6 ], and agriculture [ 7 ]. ML has now become an essential tool in the healthcare sector to aid with patient diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick¹,

Aruna

Samikannu

et al. 2022

Computational and Mathematical Methods in Medicine

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Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.

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Section: Introductionmentioning

confidence: 99%

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick¹,

Aruna

Samikannu

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

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Predicting stroke risk: An effective stroke prediction model based on neural networks

Gupta,

Mishra,

Jatana

et al. 2025

Journal of Neurorestoratology

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Heart disease prediction (HDP) using Artificial Intelligence and IoMT for intelligent healthcare models

Syed Anwar Hussainy,

Thillaigovindan,

Sabhanayagam

2023

IFS

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The present growth in Internet of Medical Things (IoMT) and Artificial Intelligence (AI) paved a way for advanced healthcare systems from conventional methods. The integration of AI and IoMT provides varied chances in medical domain. With that concern, the proposed model derives a novel model for Heart Disease Prediction (HDP), incorporates IoMT and AI. The proposed model comprises of different phases of functions, as, data collection, data preparation, feature optimization and selection, classification. IoMT devices include medical or wearable sensors are used for continuous collection of medical statistics while machine learning model process the data for disease prediction. Here, a new feature selection model called Enhanced Binary Particle Swarm Optimization (EBPSO) for reducing joint feature selection problems. With the extracted features, classification is performed with Cascaded Long Short Term Memory (CLSTM) model for attaining better accuracy of medical data classification. During evaluation, the proposed HDP model achieved the maximal accuracy in disease prediction. Hence, the model can be effectively used for diagnosing heart disease in Smart Healthcare Models.

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Development and evaluation of the bootstrap resampling technique based statistical prediction model for Covid-19 real time data : A data driven approach

Cited by 3 publications

References 19 publications

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Predicting stroke risk: An effective stroke prediction model based on neural networks

Heart disease prediction (HDP) using Artificial Intelligence and IoMT for intelligent healthcare models

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