Comparative Study and Analysis of Health Care System for Heart Patients: Formal Review
Bibliographic record
Abstract
Nationwide in today's day to life the fundamental root causes of deaths and hospitalization is heart diseases which will put enormous economic burden on health care system and on the Human life. Prediction at early stage for this type of crucial diseases is one of the best results in morbidity which can be achieve by Clinical Data Analysis. The assessment of cardiovascular events relies on numerous risk factors. Central to effective cardiovascular disease prevention is risk evaluation. In medical systems, data mining and machine learning techniques play a crucial role in developing models to predict heart or cardiovascular disease. These techniques assist researchers in estimating the likelihood of heart disease in vulnerable patients and comparing various ML models. Researchers and practitioners have proposed multiple solutions at both data and algorithm levels over time. This study employs a formal literature review (FLR) approach to provide a comprehensive overview of existing literature and uncover challenges related to imbalanced data in heart disease predictions. Prior to writing, an extensive analysis was conducted using 100 reference papers from reputable journals published between 2017 and 2023. A thorough examination of 52 referenced papers was performed, considering factors such as heart disease type, algorithms, applications, and solutions across various scientific databases, including the Ryerson University Library and Archives (RULA) online system. The primary search databases accessed through RULA were PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, Web of Science, Hindawi, and Frontiers. The FLR study revealed that current approaches face several unresolved issues when handling heart disease datasets.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".