Quantum Machine Learning for Early Detection of Chronic Diseases - Silva, Pedro; Costa, Bruna; Lima, Rafaela (Yayasan Adra Karima Hubbi, 2024)
The background of this research focuses on t, Malaysiahe development of early detection methods for chronic diseases using Quantum Machine Learning (QML). Chronic diseases such as diabetes, hypertension, heart disease, and cancer are often detected too late, leading to preventable complications. This study aims to explore the potential of QML in improving the accuracy and speed of diagnosis by combining clinical data and medical images. The method used involves the application of quantum machine learning algorithms to analyze medical datasets that include numerical information and medical images such as CT scans and MRIs. The results show that QML can process data faster and more accurately than traditional machine learning methods. QML is also capable of detecting hidden patterns in data that cannot be found with conventional techniques. The conclusion of this study shows that Quantum Machine Learning offers an effective new approach for the early detection of chronic diseases. This technology can improve healthcare systems by providing faster and more accurate predictions, which can reduce mortality rates from chronic diseases. Further research is needed to expand QML applications and address current hardware limitations