Quantum-Enhanced Machine Learning, QuEML, Outperforms Traditional Methods in Heart Disease Prediction

Quantum-Enhanced Machine Learning (QuEML) is proving to be a promising tool in predicting heart disease, according to a study that compared its performance with traditional machine learning algorithms. Using the Kaggle heart disease dataset, QuEML predicted 50.03% of positive samples correctly and 44.65% of negative samples, slightly outperforming traditional methods. Moreover, QuEML’s training time was significantly faster.

The study suggests that the integration of quantum computing can enhance machine learning algorithms, offering greater accuracy and speed in disease prediction. Future research is recommended to further improve QuEML’s accuracy and speed in predicting heart disease.

What is the Impact of Quantum-Enhanced Machine Learning on Heart Disease Prediction?

The advent of quantum technology has brought about new possibilities for machine learning algorithms, particularly in the healthcare industry. These algorithms are now being used to diagnose complex health disorders such as heart disease. This article focuses on the effectiveness of Quantum-Enhanced Machine Learning (QuEML) in predicting heart disease.

The performance of QuEML was evaluated against traditional machine learning algorithms using the Kaggle heart disease dataset, which contains 1190 samples. The results showed that QuEML predicted around 50.03% of positive samples as positive and an average of 44.65% of negative samples as negative. In comparison, traditional machine learning approaches could predict around 49.78% of positive samples as positive and 44.31% of negative samples as negative.

Furthermore, the computational complexity of QuEML was measured, which consumed an average of 670 µs for its training, whereas traditional machine learning algorithms could consume an average 8625 µs for training. Hence, QuEML was found to be a promising approach in heart disease prediction with an accuracy rate of 0.6% higher and training time of 1925 µs faster than that of traditional machine learning approaches.

How Prevalent is Heart Disease and What are the Current Diagnostic Methods?

Heart disease is a leading cause of death worldwide. Changes in food habits, lifestyle, and work-related stress are major contributors to the increase in the rate of heart disease. The World Health Organization (WHO) stated that 17.7 million people all around the world suffer from heart disease every year. In India alone, 17 million people were affected by cardiovascular diseases (CVD) in 2016.

Diagnosis of heart disease can be done by manual examination of risk factors such as patients’ age, sex, family history, lifestyle, etc., physical examination reports, and analyzing the patients’ symptoms. However, manual examination can lead to inaccurate prediction since some parameters to be analyzed may remain hidden and it is computationally expensive to analyze such huge factors. Angiography is widely used as the most precise method for diagnosing CAD, but it is associated with high cost and major side effects.

Healthcare sectors are struggling to offer reliable diagnoses at a reasonable cost. Moreover, image-based detections are more costly and not suitable for screening large populations. So many researchers have endeavored to develop a noninvasive, economical, and fast automated diagnosis system for early detection of CAD based on Machine Learning Algorithms.

How Does Quantum Computing Enhance Machine Learning Algorithms?

Many Machine Learning (ML) algorithms have shown promising results in terms of greater accuracy rate and speed up the performance in early diagnosis of heart disease by identifying hidden patterns. Despite these greater benefits, there is a computational bottleneck when dealing with larger and more complex data using traditional computers results ML algorithms being incapable of handling computationally rich tasks.

At this point, the quantum computing principle has lent its hand to enhance computational power. Thus, quantum Enhanced machine learning algorithms facilitate healthcare industries to evaluate and treat complicated health disorders. The major contribution of this article is conducting an experimental study about various ML algorithms that have been utilized recently in predicting heart disease from which suitable ML methods have been identified.

Then this study revealed the essentials of quantum computing integration to enhance the machine learning algorithms to speed up the computation process. Then the proposed QuEML techniques have been evaluated based on selected features that show greater accuracy and speed in prediction.

What are the Findings and Future Directions of this Study?

The paper begins by providing an overview of the various machine-learning algorithms used for heart diagnosis. This is followed by a detailed study of ML and QML algorithms to explore their strengths and weakness in detecting disease. Further, an explanation of the workings of Quantum Enhanced Machine Learning Algorithms (QML) is presented.

The experimental results obtained from utilizing the datasets are then presented to demonstrate the effectiveness of the diagnostic methods. The paper concludes by summarizing the findings and highlighting potential areas for future improvement.

The findings of this study suggest that Quantum-Enhanced Machine Learning (QuEML) is a promising approach in heart disease prediction with an accuracy rate of 0.6% higher and training time of 1925 µs faster than that of traditional machine learning approaches. Future research should focus on further improving the accuracy and speed of QuEML in predicting heart disease.