Risk Prediction Of Chronic Kidney Disease Using Machine Learning Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CKD is a serious reason of demise and disability. It was the 27th focal reason in 1990 and became 18th focal reason in 2010. Near about 1 million people lose their life in 2013. In spite of that, people of developing countries are being affected by CKD. We analyzed the data of CKD patient and proposed a system from which it will be possible to predict the risk of CKD. We have used 455 patients' data. Online data set which is collected from UCI Machine Learning Repository and real time dataset which is collected from Khulna City Medical College are used here. We used Python as a high-level interpreted programming language for developing our system. We trained the data using 10-fold CV and applied Random forest and ANN. The accuracy achieved by Random forest algorithm is 97.12% and ANN is 94.5%. This system will help to predict early disclosure of chronic kidney diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it