A Novel Heart Disease Prediction System Based on Quantum Neural Network Using Clinical Parameters

Aims: The diagnosis of Heart disease at earliest possible stage is very crucial to increase the chance of successful treatment and to reduce the mortality rate. The interpretation of cardiovascular disease is time-consuming and requires analysis by an expert physician. Thus there is a need of expert system which may provide quick and accurate prediction of Heart disease at early possible stage, without the help of physician.

Place and Duration of Study: The study was carried out during 2010 to 2013 in the vicinity of Yamuna Nagar, Haryana, India.

Methodology: The data used for this study consists of clinical values (Diabetes Mellitus, Low Density Lipoprotein, Triglycerides and High Density Lipoprotein) and has been collected from various Hospitals of 689 patients, who have symptoms of heart disease. All these cases are analyzed after careful scrutiny with the help of the Physicians. For training and evaluation purpose we have carefully predicted the level of heart disease by taking the help of Cardiologist/ Physician. The data consists of patients’ record with doctor’s predictions/ diagnosis.

Results: The obtained result of Heart disease prediction match with the expert physician’s opinion with 96.97% accuracy and shows high degrees of sensitivity and specificity.

Conclusion: The proposed Heart Disease Prediction System based on Quantum Neural Network gives the high degrees of accuracy in predicting the risk of cardiovascular diseases, are also the best results based on clinical factors. The result generated by this system has been evaluated and validated on data of patients with the Doctor’s diagnosis. This system will help the doctors to plan for a better medication and provide the patient with early diagnosis as it performs reasonably well even without retraining. Such an expert system may also prove useful in combination with other systems to providing diagnostic and predictive medical opinions in a timely manner.