Biosensor based Detection of Early Spread of Vector Borne Disease with Personalized Treatment strategies using Machine Learning
Bibliographic record
Abstract
Vector-borne diseases in India show growing patterns which strongly affect the population of the nation.The government faces a major obstacle in disease prevention efforts.Every year many people throughout India suffer from these illnesses.The physical differences between geographic regions and ways of life make it difficult for current strategies to control diseases during their initial development stages.The project focuses on creating advanced methods based on machine learning to diagnose diseases caused by vectors.The planned investigation targets dengue rather than other vector-borne diseases because it has emerged as one of the most dominant pathologies in contemporary years.A total of five stages make up the proposed methodology beginning with Data Transformation after which Preprocessing occurs followed by Feature Scaling and Normalization and finally Dataset Partitioning to allow Model Development and Prediction.The proposed model brings forth an ability to identify dengue fever development throughout its stages.The proposed solution stands out because it identifies dengue fever during early stages while determining the disease severity using patient clinical information.A test of the model used Support Vector Machine (SVM), Decision Tree and Gaussian Naïve Bayes Classifier, Logistic Regression and Random Forest Classifier algorithms for evaluation.A biosensor was used for extensive testing and validation which enabled the suggested technique to produce a 97.5% accuracy rate.The Gaussian Naïve Bayes classifier achieved 97.5% accuracy although it had a root mean square error value of zero.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".