Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach
Renu Narain1, Sanjai Saxena1, Achal Kumar Goyal2
1Department of Biotechnology, Thapar University, Punjab, India.
Insights
A new machine learning system accurately predicts cardiovascular disease (CVD) risk, achieving 98.57% accuracy. This advanced tool surpasses the outdated Framingham risk score (FRS), offering improved early diagnosis and treatment planning for medical practitioners.
Area of Science:
- Medical Informatics
- Machine Learning
- Cardiovascular Health
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality.
- The Framingham risk score (FRS) is a common but outdated predictor for CVD risk.
- There is a need for more accurate CVD prediction methods for modern populations.
Purpose of the Study:
- To develop and evaluate a novel machine learning system for predicting cardiovascular disease (CVD) risk.
- To compare the proposed system's accuracy against the traditional Framingham risk score (FRS).
Main Methods:
- Utilized data from 689 patients with CVD symptoms and validated with 5,209 patients from the Framingham study.
- Employed a quantum neural network for machine learning to identify CVD patterns.
- Experimentally evaluated the system's performance and compared it with FRS.
Main Results:
- The proposed system achieved a high accuracy of 98.57% in predicting CVD risk.
- Validated predictions against the Framingham study dataset, confirming its ability to identify risks and actual outcomes.
- Demonstrated significantly higher accuracy compared to FRS and other existing methods.
Conclusions:
- The novel machine learning system is an effective tool for medical practitioners in predicting CVD risk.
- Enables better medication and treatment strategy planning through early and accurate diagnosis.
- Achieved 98.57% accuracy, significantly outperforming FRS and supporting its adoption for improved patient care.
Purpose:
Currently cardiovascular diseases (CVDs) are the main cause of death worldwide. Disease risk estimates can be used as prognostic information and support for treating CVDs. The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough. In this paper, a novel CVD prediction system based on machine learning is proposed.
Methods:
This study has been conducted with the data of 689 patients showing symptoms of CVD. Furthermore, the dataset of 5,209 CVD patients of the famous Framingham study has been used for validation purposes. Each patient's parameters have been analyzed by physicians in order to make a diagnosis. The proposed system uses the quantum neural network for machine learning. This system learns and recognizes the pattern of CVD. The proposed system has been experimentally evaluated and compared with FRS.
Results:
During testing, patients' data in combination with the doctors' diagnosis (predictions) are used for evaluation and validation. The proposed system achieved 98.57% accuracy in predicting the CVD risk. The CVD risk predictions by the proposed system, using the dataset of the Framingham study, confirmed the potential risk of death, deaths which actually occurred and had been recorded as due to myocardial infarction and coronary heart disease in the dataset of the Framingham study. The accuracy of the proposed system is significantly higher than FRS and other existing approaches.
Conclusion:
The proposed system will serve as an excellent tool for a medical practitioner in predicting the risk of CVD. This system will be serving as an aid to medical practitioners for planning better medication and treatment strategies. An early diagnosis may be effectively made by using this system. An overall accuracy of 98.57% has been achieved in predicting the risk level. The accuracy is considerably higher compared to the other existing approaches. Thus, this system must be used instead of the well-known FRS.
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