ArticleBioData mining2022
Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection.
Article in BioData mining, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Distinguishing common respiratory pathogens using machine learning of symptom profiles to prioritize diagnostic testing.PLOS digital health · 2026Article
- A Precise Apple Quality Prediction Model Integrating Driving Factor Screening and BP Neural Network.Plants (Basel, Switzerland) · 2025Article
- A new ensemble heart attack diagnosis (EHAD) model using artificial intelligence techniques.Scientific reports · 2025Article
- Article
- The prediction of influenza-like illness using national influenza surveillance data and Baidu query data.BMC public health · 2024Article
- A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention.International journal of environmental research and public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Symptom-based machine learning models for disease detection are a way to reduce the workload of doctors when they have too many patients. Currently, there are many research studies on machine learning or deep learning for disease detection or clinical departments classification, using text of patient's symptoms and vital signs. In this study, we used the Long Short-term Memory (LSTM) with a fully connected neural network model for classification, where the LSTM model was used to receive the patient's symptoms text as input data. The fully connected neural network was used to receive other input data from the patients, including body temperature, age, gender, and the month the patients received care in. In this research, a data preprocessing algorithm was improved by using keyword selection to reduce the complexity of input data for overfitting problem prevention. The results showed that the LSTM with fully connected neural network model performed better than the LSTM model. The keyword selection method also increases model performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.