A novel quantum neural network-based system achieved 98.57% accuracy in predicting cardiovascular disease risk, outperforming the Framingham risk score.
Observational (n=5,898)
Does a quantum neural network based machine learning system improve accuracy in predicting CVD risk compared to the Framingham risk score in patients with or at risk for CVD?
A novel quantum neural network machine learning model demonstrated 98.57% accuracy for cardiovascular risk prediction, outperforming the traditional Framingham risk score.
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 The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough.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.
Narain et al. (Fri,) conducted a observational in Cardiovascular disease (n=5,898). Quantum neural network based CVD prediction system vs. Framingham risk score (FRS) was evaluated on Accuracy in predicting CVD risk. A novel quantum neural network-based system achieved 98.57% accuracy in predicting cardiovascular disease risk, outperforming the Framingham risk score.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: