Finding Nutritional Deficiency and Disease Pattern of Rural People Using Fuzzy Logic and Big Data Techniques on Hadoop
Bibliographic record
Abstract
Over the decades there is a high demand of a tool to identify the nutritional needs of the people of Bangladesh since it has an alarming rate of under nutrition among the countries of the world. This analysis has focused on the dissimilarity of diseases caused by malnutrition in different districts of Bangladesh. Among the 64 districts, there is no single one found where people have grown proper nutritional food habit. Low income and less knowledge are the triggering factors and the case is worse in the rural areas. In this research, a distributed enumerating framework for large data set is processed in big data models. Fuzzy logic has the ability to model the nutrition problem, in the way helping people to calculate the suitability between food calories and user’s profile. A Map Reduce-based K-nearest neighbor (mrK-NN) classifier has been applied in this research in order to classify data. We have designed a balanced model applying fuzzy logic and big data analysis on Hadoop concerning food habit, food nutrition and disease, especially for the rural people.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".