Data science for healthcare predictive analytics

Leung, Carson K.; Fung, Daryl L. X.; Mushtaq, Saad B.; Leduchowski, Owen T.; Bouchard, Robert Luc; Jin, Hui; Cuzzocrea, Alfredo; Zhang, Christine Y.

doi:10.1145/3410566.3410598

Cited by 14 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Predictive analytics is a field of data analytics involving engineering, scientific, mathematical, and statistical models and data mining techniques, as well as AI and machine learning methods, for foreseeing patterns to make accurate and meaningful decisions [ 41 ]. The past and real-time data are used to predict the future.…”

Section: Methodsmentioning

confidence: 99%

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit

et al. 2023

View full text Add to dashboard Cite

The ongoing fast-paced technology trend has brought forth ceaseless transformation. In this regard, cloud computing has long proven to be the paramount deliverer of services such as computing power, software, networking, storage, and databases on a pay-per-use basis. The cloud is a big proponent of the internet of things (IoT), furnishing the computation and storage requisite to address internet-of-things applications. With the proliferating IoT devices triggering a continual data upsurge, the cloud–IoT interaction encounters latency, bandwidth, and connectivity restraints. The inclusion of the decentralized and distributed fog computing layer amidst the cloud and IoT layer extends the cloud’s processing, storage, and networking services close to end users. This hierarchical edge–fog–cloud model distributes computation and intelligence, yielding optimal solutions while tackling constraints like massive data volume, latency, delay, and security vulnerability. The healthcare domain, warranting time-critical functionalities, can reap benefits from the cloud–fog–IoT interplay. This research paper propounded a fog-assisted smart healthcare system to diagnose heart or cardiovascular disease. It combined a fuzzy inference system (FIS) with the recurrent neural network model’s variant of the gated recurrent unit (GRU) for pre-processing and predictive analytics tasks. The proposed system showcases substantially improved performance results, with classification accuracy at 99.125%. With major processing of healthcare data analytics happening at the fog layer, it is observed that the proposed work reveals optimized results concerning delays in terms of latency, response time, and jitter, compared to the cloud. Deep learning models are adept at handling sophisticated tasks, particularly predictive analytics. Time-critical healthcare applications reap benefits from deep learning’s exclusive potential to furnish near-perfect results, coupled with the merits of the decentralized fog model, as revealed by the experimental results.

show abstract

Section: Methodsmentioning

confidence: 99%

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The synergy between data scientists and the biomedical communities is helpful to improve the detection of diseases and illnesses, as well as the prediction of possible complications. Authors in [22] developed a framework to predict cancer trends accurately. This type of framework could be used for the analysis of other clinical data.…”

Section: Related Workmentioning

confidence: 99%

Efficient analysis of COVID-19 clinical data using machine learning models

Ali

Zhou

Patterson

2022

Med Biol Eng Comput

View full text Add to dashboard Cite

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before by leveraging such big data. This data is of many different varieties and can be of different levels of veracity, e.g., precise, imprecise, uncertain, and missing, making it challenging to extract meaningful information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -often in real-time -the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning-based algorithms to this big data is a natural approach to take to this aim since they can quickly scale to such data and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19 and potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation and then propose a model that first performs efficient feature selection from such representation. We apply this approach to two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policymakers focus on only certain attributes to study this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.

show abstract

“…The synergy between data scientists and the biomedical communities is helpful to improve the detection of diseases and illnesses, as well as the prediction of possible complications. Authors in [22] developed a framework with to predict cancer trends accurately. This type of framework could be used for the analysis of other clinical data.…”

Section: Related Workmentioning

confidence: 99%

Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

Ali¹,

Zhou²,

Patterson³

2021

Preprint

View full text Add to dashboard Cite

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before, by leveraging such big data. This data is of many different varieties, and can be of different levels of veracity e.g., precise, imprecise, uncertain, and missing, making it challenging to extract important information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -often in real-time -the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19, and for potential future pandemics in general.In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation, and then propose a model that first performs efficient feature selection from such representation. We apply this approach on two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most

show abstract

Data science for healthcare predictive analytics

Cited by 14 publications

References 20 publications

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit

Efficient analysis of COVID-19 clinical data using machine learning models

Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

Contact Info

Product

Resources

About