LSTM-RNN Based Approach for Prediction of Dengue Cases in India
Bibliographic record
Abstract
Data driven health care research is accentuated due to the Gargantua data available in the form of structured and unstructured. Forecasting the impact of infectious and epidemic diseases will be of major assistance to the health care industry. The objective of this work is to study the impact of dengue cases across India by applying the Deep Learning methodologies based on Long Short-Term Memory using Recurrent Neural Networks. The factors considered in the prediction method are climatic conditions, temperature, rainfall data, humidity and population considered for the period between 2014 and 2019. The activation function applied is ReLU and on training the model using LSTM the level of accuracy in forecasting the epidemic is over 89% for infection and for death it is 81%. The Root Mean Square Error values are also computed and it is observed that when the number of iterations is increased the error value decreased. The proposed methodology assists the Health care department to make safety precautions before the outbreak of the dengue fever.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".