An Efficient and Scalable Heart Disease Diagnosis System with Attribute Impact Based Weights and Genetic Correlation Analysis
Bibliographic record
Abstract
Fuzzy neural network (FNN) is playing a vital role in processing of complex data mining applications like medical diagnosis, speech recognition, text processing, image processing etc. Fuzzy neural networks simulate the human brain functionality with fuzzy logic decision making capabilities, to achieve more accuracy in feature selection process of complex data mining applications. Today cardiovascular diseases become a serious global health issue and approximately more than 31% of all global deaths are happening due to cardiovascular diseases reported by WHO. In order to prevent and control the cardiovascular diseases, an efficient and accurate heart disease diagnosis system (HDDS) has to be designed with the state of the art feature based data classifiers. In recent, some research articles introduced HDDS using popular data mining techniques like FNN, but they are suffering from accuracy in allocation of attribute weights and attribute correlation analysis, pattern recognition, forecasting. To address the problems in designing the HDDS, in this paper, Fuzzy Neural Networks has been used with empowered input layer and hidden layers to achieve the high accuracy and performance, while processing the huge set of medical data records. We designed an Attribute Impact calculation procedure to assign the accurate weight values to the attributes and we proposed a Genetic Correlation Analysis algorithm to do correlation analysis which helps in improving the performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".