Heart Disease Prediction Using Machine Learning Method

Rasheed, Mafia; Khan, Muhammad Adnan; Elmitwally, Nouh Sabri; Issa, Ghassan F.; Ghazal, Taher M.; Alrababah, Hamza; Mago, Beenu

doi:10.1109/iccr56254.2022.9995736

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Te presence of a healthcare issue is indicated by the fact that 211 samples are processed appropriately forecast as negative. Even though a healthcare issue exists, nine (9) samples are wrongly forecasted as positive, indicating the absence of a healthcare issue. In the validation phase, Table 4 illustrates the LM model prediction for lung cancer illness.…”

Section: Resultsmentioning

confidence: 99%

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Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Abbas

Issa

Fatima

et al. 2023

International Journal of Intelligent Systems

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In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state-of-the-art published methods.

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Section: Resultsmentioning

confidence: 99%

“…Heart disease prediction [9] with the help of machine learning is studied for the wellbeing of humans.…”

Section: Introductionmentioning

confidence: 99%

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Abbas

Issa

Fatima

et al. 2023

International Journal of Intelligent Systems

Self Cite

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“…It also raises blood pressure. [9]. The RF, KNN, LR, NB, GB, and AB machine learning (ML) algorithms were utilized in the study to predict cardiac disease [10].…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Prediction Using GridSearchCV and Random Forest

Rasheed,

Kiran Kumar,

Rani

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: This study explores machine learning algorithms (SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest) for heart disease prediction, utilizing comprehensive cardiovascular and clinical data. Our research enables early detection, aiding timely interventions and preventive measures. Hyperparameter tuning via GridSearchCV enhances model accuracy, reducing heart disease's burdens. Methodology includes preprocessing, feature engineering, model training, and cross-validation. Results favor Random Forest for heart disease prediction, promising clinical applications. This work advances predictive healthcare analytics, highlighting machine learning's pivotal role. Our findings have implications for healthcare and policy, advocating efficient predictive models for early heart disease management. Advanced analytics can save lives, cut costs, and elevate care quality. OBJECTIVES: Evaluate the models to enable early detection, timely interventions, and preventive measures. METHODS: Utilize GridSearchCV for hyperparameter tuning to enhance model accuracy. Employ preprocessing, feature engineering, model training, and cross-validation methodologies. Evaluate the performance of SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest algorithms. RESULTS: The study reveals Random Forest as the favored algorithm for heart disease prediction, showing promise for clinical applications. Advanced analytics and hyperparameter tuning contribute to improved model accuracy, reducing the burden of heart disease. CONCLUSION: The research underscores machine learning's pivotal role in predictive healthcare analytics, advocating efficient models for early heart disease management.

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“…Convolutional Neural Network (CNN) was trained to classify the ECG waveform in this research. The proposed IoT framework for heart disease prediction in cloud environment based on an MDCNN classifier and MSSO-ANFIS [33], [34]can enable continuous monitoring of patients' vital signs, early detection of heart disease, and timely intervention, thereby improving patient outcomes and reducing healthcare costs is studied. The "Secure framework for authentication and encryption using ECC via IoT-Based Medical Sensor Data [35]" is a proposed system for securing the transmission of medical sensor data in the context of the Internet of Things (IoT).The authors suggest that their proposed framework can help to mitigate security risks associated with the transmission of sensitive medical data, such as the unauthorized access, modification, or interception of the data.…”

Section: Introductionmentioning

confidence: 99%

Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

et al. 2023

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Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMTbased approach is considered. The google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.INDEX TERMS IoMT, transfer learning, deep machine learning, histopathology, image processing, lung cancer.

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Heart Disease Prediction Using Machine Learning Method

Cited by 7 publications

References 16 publications

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Heart Disease Prediction Using GridSearchCV and Random Forest

Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

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