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<p>Big data analytics has increasingly penetrated the medical industry due to the swift growth of Internet technology along with medical data digitalization, hospital information systems, a huge count of Electronic Health Records (EHR) and other emerging data. The big data’s potential in healthcare is mainly based on its capability of detecting patterns and turning the high volume of data into actionable knowledge for decision-makers. Considering the applications of big data analytics in the medical industry, we have introduced an improved RNN-based big data healthcare monitoring system including the following working stages. Firstly, acquired data gets pre-processed by an outlier detection process. Afterwards, Improved SMOTE (Synthetic Minority Oversampling Technique) based class imbalance processing is performed to get the balanced data. This balanced data is handled using the Spark framework, with the master node carrying out an improved Deep Fuzzy Clustering (DFC) based clustering process and the slave node handling feature extraction and an enhanced Support Vector Machine Recursive Feature Elimination (SVM-RFE) based feature selection process. To divide the data according to the patient’s condition, an enhanced Deep Autoencoder-based Fuzzy C Means Clustering (DAE-FCM) is suggested in the improved DFC. Features including statistics, enhanced entropy, and mutual information are extracted throughout the feature extraction process. Ultimately, an Improved Recurrent Neural Network (RNN) is used to classify diseases using the chosen feature from the slave node. The implementation outcomes proved that the proposed big data healthcare monitoring system can provide effective and accurate disease classification. The Improved RNN method demonstrated the highest accuracy, achieving an impressive rate of 0.945 for dataset 1 and 0.947 for dataset 2 at training data 80%, while the conventional methods acquired the least accuracy ratings. By integrating advanced techniques, the suggested system offers a framework for data-driven decision-making in healthcare in addition to increasing the effectiveness of disease classification. This has the potential to revolutionize patient monitoring, early diagnosis, and overall healthcare delivery, ultimately leading to better health outcomes.</p>

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Big Data Healthcare Monitoring System: Improved Recurrent Neural Network for Disease Classification with Apache Spark

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Amruta Prabhugouda,
  • Syeda Asra

摘要

Big data analytics has increasingly penetrated the medical industry due to the swift growth of Internet technology along with medical data digitalization, hospital information systems, a huge count of Electronic Health Records (EHR) and other emerging data. The big data’s potential in healthcare is mainly based on its capability of detecting patterns and turning the high volume of data into actionable knowledge for decision-makers. Considering the applications of big data analytics in the medical industry, we have introduced an improved RNN-based big data healthcare monitoring system including the following working stages. Firstly, acquired data gets pre-processed by an outlier detection process. Afterwards, Improved SMOTE (Synthetic Minority Oversampling Technique) based class imbalance processing is performed to get the balanced data. This balanced data is handled using the Spark framework, with the master node carrying out an improved Deep Fuzzy Clustering (DFC) based clustering process and the slave node handling feature extraction and an enhanced Support Vector Machine Recursive Feature Elimination (SVM-RFE) based feature selection process. To divide the data according to the patient’s condition, an enhanced Deep Autoencoder-based Fuzzy C Means Clustering (DAE-FCM) is suggested in the improved DFC. Features including statistics, enhanced entropy, and mutual information are extracted throughout the feature extraction process. Ultimately, an Improved Recurrent Neural Network (RNN) is used to classify diseases using the chosen feature from the slave node. The implementation outcomes proved that the proposed big data healthcare monitoring system can provide effective and accurate disease classification. The Improved RNN method demonstrated the highest accuracy, achieving an impressive rate of 0.945 for dataset 1 and 0.947 for dataset 2 at training data 80%, while the conventional methods acquired the least accuracy ratings. By integrating advanced techniques, the suggested system offers a framework for data-driven decision-making in healthcare in addition to increasing the effectiveness of disease classification. This has the potential to revolutionize patient monitoring, early diagnosis, and overall healthcare delivery, ultimately leading to better health outcomes.