Prognostic Evaluation, Prediction and Regimen of Diseases Using SVM, NB and RF Classifiers
Bibliographic record
Abstract
Early diagnosis and prognosis of deadly illnesses have been made possible by advancements in machine learning algorithms.In order to analyze patient pathology reports and make informed decisions regarding medicine supply and marketing strategies, pharmaceutical companies employ advanced data mining tools to generate statistical reports and extract valuable information.Our proposed system fulfils the requirement of patients as well as pharmaceutical clients that are concerned with the two diseases: diabetes mellitus and hypothyroidism.Generating statistical reports from the relevant data and providing an aerial view of the occurrence and spread of a disease in India.The diabetes mellitus and hypothyroidism prediction is carried out using three models: Support Vector Machine (SVM), Naive Bayes, and Random Forest (RF).The Random Forest model is the most appropriate for predicting diabetes mellitus with an accuracy of 90% and 98.05% for predicting hypothyroidism.Diabetes increases a patient's risk of heart disease, stroke and vision problems.Hence our findings help patients to take proactive care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".