What is the clinical evidence from this study?

Study design: Other. Population: Early disease detection. Intervention: Predictive model using machine learning algorithms (Random Forest, SVM, C5.0, Naive Bayes, ANN).

August 15, 2019Applied SciencesOpen Access

iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †

Q: What does this research mean for the field?

A probabilistic data acquisition scheme and machine learning-based prediction model can effectively integrate and analyze patient medical, emotional, and genetic data for early disease detection. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

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Key result

A probabilistic data acquisition scheme and prediction model using machine learning algorithms was designed to analyze patient medical, emotional, and genetic data for early disease detection.

Why the study?

There is a need to develop a smart and interactive system to analyze massive unstructured or semi-structured biological data for early disease detection.

Comparison

Random Forest vs Support Vector Machine (SVM) vs C5.0 vs Naive Bayes vs Artificial Neural Networks

Authors

MBMd. Ataur Rahman BhuiyanBRAC University MUMd. Rifat UllahIndiana University – Purdue University Indianapolis ADAmit Kumar DasNorth South University

Discussion

Member takes

Implication

Supports multimodal ML model development for early detection; leaves open prospective validation before clinical adoption.

Structured PICO

Intervention

Predictive model using Random Forest, Support Vector Machine (SVM), C5.0, Naive Bayes, and Artificial Neural Networks

Outcome

Disease prediction performance

The study proposes a probabilistic data acquisition scheme and compares various machine learning algorithms for disease prediction using big data in healthcare.

Cite This Study

Bhuiyan et al. (2019) studied Early disease detection. Predictive model using machine learning algorithms (Random Forest, SVM, C5.0, Naive Bayes, ANN) was evaluated. A probabilistic data acquisition scheme and prediction model using machine learning algorithms was designed to analyze patient medical, emotional, and genetic data for early disease detection.

synapsesocial.com/papers/6a18ea8bd654b1eb0d4b0bcb https://doi.org/10.3390/app9163365

Topics

Artificial intelligence in cardiology

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August 15, 2019Applied SciencesOpen Access

iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †

View Full Paper

Ask AI

Bookmark

Key result

A probabilistic data acquisition scheme and prediction model using machine learning algorithms was designed to analyze patient medical, emotional, and genetic data for early disease detection.

Why the study?

There is a need to develop a smart and interactive system to analyze massive unstructured or semi-structured biological data for early disease detection.

Comparison

Random Forest vs Support Vector Machine (SVM) vs C5.0 vs Naive Bayes vs Artificial Neural Networks

Authors

MBMd. Ataur Rahman BhuiyanBRAC University MUMd. Rifat UllahIndiana University – Purdue University Indianapolis ADAmit Kumar DasNorth South University

Discussion

Member takes

Implication

Supports multimodal ML model development for early detection; leaves open prospective validation before clinical adoption.

Structured PICO

Intervention

Predictive model using Random Forest, Support Vector Machine (SVM), C5.0, Naive Bayes, and Artificial Neural Networks

Outcome

Disease prediction performance

The study proposes a probabilistic data acquisition scheme and compares various machine learning algorithms for disease prediction using big data in healthcare.

Cite This Study

synapsesocial.com/papers/6a18ea8bd654b1eb0d4b0bcb https://doi.org/10.3390/app9163365

Also Consider

Synapse has enriched one closely related paper. Consider it for comparative context:

1System Design for Big Data Application in Emotion-Aware Healthcare2016 · 87 citations

Topics

Artificial intelligence in cardiology

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