  Heart disease morbidity and mortality rates are increasing, which has a
negative impact on public health and the global economy. Early detection of
heart disease reduces the incidence of heart mortality and morbidity. Recent
research has utilized quantum computing methods to predict heart disease with
more than 5 qubits and are computationally intensive. Despite the higher number
of qubits, earlier work reports a lower accuracy in predicting heart disease,
have not considered the outlier effects, and requires more computation time and
memory for heart disease prediction. To overcome these limitations, we propose
hybrid random forest quantum neural network (HQRF) using a few qubits (two to
four) and considered the effects of outlier in the dataset. Two open-source
datasets, Cleveland and Statlog, are used in this study to apply quantum
networks. The proposed algorithm has been applied on two open-source datasets
and utilized two different types of testing strategies such as 10-fold cross
validation and 70-30 train/test ratio. We compared the performance of our
proposed methodology with our earlier algorithm called hybrid quantum neural
network (HQNN) proposed in the literature for heart disease prediction. HQNN
and HQRF outperform in 10-fold cross validation and 70/30 train/test split
ratio, respectively. The results show that HQNN requires a large training
dataset while HQRF is more appropriate for both large and small training
dataset. According to the experimental results, the proposed HQRF is not
sensitive to the outlier data compared to HQNN. Compared to earlier works, the
proposed HQRF achieved a maximum area under the curve (AUC) of 96.43% and
97.78% in predicting heart diseases using Cleveland and Statlog datasets,
respectively with HQNN. The proposed HQRF is highly efficient in detecting
heart disease at an early stage and will speed up clinical diagnosis.
