A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data
Abstract
There is a considerable rise in cardiovascular diseases in the world. It is pertinently essential to make cardiovascular prediction accurate to the maximum. A forecast based on machine learning techniques can be beneficial in detecting cardiovascular disease (CVD) with maximum precision and accuracy. The disease's effective prediction helps in early diagnosis, which cuts down the mortality rate. A health history and the causes of heart disease require the efficient detection and prediction of CVD. Data analytics is beneficial for making predictions based on a massive amount of data, and it aids health clinics in disease prognosis. Regularly, a large volume of patient-related data is maintained. The information gathered can be used to forecast the emergence of upcoming diseases. Our study presents a detailed comparative study of Cardiovascular Disease by comparing the various machine learning techniques mainly comprising of classification and predictive algorithms. The study shows an in-depth analysis of around forty-one papers related to cardiovascular disease by using machine learning techniques. This study evaluates the selected publications rigorously and identifies gaps in the available literature, making it useful for researchers to develop and apply in clinical fields, primarily on datasets related to heart disease. The current study will aid medical practitioners in predicting heart threats ahead of time, allowing them to take preventative measures.
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Preliminary Toxicity Analysis of 3DCRT versus IMRT on the High Dose Arm of the RTOG 0126 Prostate Cancer Trial
- Adaptive Radiotherapy for Head and Neck Cancer: Initial Clinical Outcomes from a Prospective Trial
- American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology
- Motion Management Strategies and Technical Issues Associated with Stereotactic Body Radiotherapy of Thoracic and Upper Abdominal Tumors: A Review from NRG Oncology
- High-dose-rate interstitial brachytherapy as a monotherapy for localized prostate cancer: treatment description and preliminary results of a phase I/II clinical trial
- Highly efficient training, refinement, and validation of a knowledge-based plan quality control system for radiotherapy clinical trials
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.