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  • Open Access


    Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization

    P. Nandakumar, R. Subhashini*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 57-75, 2024, DOI:10.32604/csse.2023.042294

    Abstract Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increased a lot. Machine learning models have been widely used for the prediction of heart disease from the different University of California Irvine (UCI) Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was… More >

  • Open Access


    Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

    Muhammad Tayyeb1, Muhammad Umer1, Khaled Alnowaiser2, Saima Sadiq3, Ala’ Abdulmajid Eshmawi4, Rizwan Majeed5, Abdullah Mohamed6, Houbing Song7, Imran Ashraf8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1677-1694, 2023, DOI:10.32604/cmes.2023.026535

    Abstract Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals with window size 10 is… More >

  • Open Access


    An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

    Shahid Mohammad Ganie1, Pijush Kanti Dutta Pramanik2, Majid Bashir Malik3, Anand Nayyar4, Kyung Sup Kwak5,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244

    Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was… More >

  • Open Access


    Modelling a Fused Deep Network Model for Pneumonia Prediction

    M. A. Ramitha*, N. Mohanasundaram, R. Santhosh

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2725-2739, 2023, DOI:10.32604/csse.2023.030504

    Abstract Deep Learning (DL) is known for its golden standard computing paradigm in the learning community. However, it turns out to be an extensively utilized computing approach in the ML field. Therefore, attaining superior outcomes over cognitive tasks based on human performance. The primary benefit of DL is its competency in learning massive data. The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully. Specifically, various DL approaches outperform the conventional ML approaches in real-time applications. Indeed, various research works are reviewed to understand the significance of the individual DL models and some computational complexity… More >

  • Open Access


    A Convolutional Neural Network Model for Wheat Crop Disease Prediction

    Mahmood Ashraf1,*, Mohammad Abrar2, Nauman Qadeer3, Abdulrahman A. Alshdadi4, Thabit Sabbah5, Muhammad Attique Khan6

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3867-3882, 2023, DOI:10.32604/cmc.2023.035498

    Abstract Wheat is the most important cereal crop, and its low production incurs import pressure on the economy. It fulfills a significant portion of the daily energy requirements of the human body. The wheat disease is one of the major factors that result in low production and negatively affects the national economy. Thus, timely detection of wheat diseases is necessary for improving production. The CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop diseases. However, these models are computationally expensive and need a large amount of training data. In this research, a light weighted modified CNN architecture… More >

  • Open Access


    Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

    V. Gowri1,*, V. Vijaya Chamundeeswari2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3675-3690, 2023, DOI:10.32604/iasc.2023.035817

    Abstract Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data. But, the traditional decision forest (DF) algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively. In this work, we propose a bootstrap decision forest using penalizing attributes (BFPA) algorithm to predict heart disease with higher accuracy. This work integrates a significance-based attribute selection (SAS) algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness. The proposed SAS algorithm is used to determine the correlation among attributes and to select the optimum… More >

  • Open Access


    Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

    Sathya Bama B*, Bevish Jinila Y

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2085-2097, 2023, DOI:10.32604/iasc.2023.028481

    Abstract Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson’s… More >

  • Open Access


    Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

    S. Amutha*, J. Raja Sekar

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1863-1880, 2023, DOI:10.32604/iasc.2023.028903

    Abstract In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are fed to the deep neural… More >

  • Open Access


    Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach

    Ghada Abdulsalam1, Souham Meshoul2,*, Hadil Shaiba3

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 761-779, 2023, DOI:10.32604/iasc.2023.032262

    Abstract Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed… More >

  • Open Access


    Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

    C. Santhosh Kumar1, K. Vishnu Kumar2,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2893-2907, 2023, DOI:10.32604/iasc.2023.029669

    Abstract Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy and delay aware computational offloading… More >

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