One of the sectors that deep learning has made a tremendous impact is in healthcare [1]. In fact, researches on the medical sector involve deep learning methods in their analyses. Anyone could agree that it is imperative that disease diagnosis early on the treatment is equally if not more important as the treatment itself. Therefore, these advancements when applied to the health sector are really helpful in saving people’s lives since one of the benfits of a carefully trained deep learning model is its accuracy and speed in generating results. Truly, this will not only benefit patients but doctors as well [2].
Out of all the diseases that need utmost priority of the medical sector as well as researchers in the field of healthcare, heart disease is the one that needs careful attention. In the United States, heart disease is the leading cause of death for men, women and primary racial and ethnic groups [3]. Alarmingly, one person dies every 37 seconds in the United States from heart disease and about 647,000 Americans die from heart disease each year [3,4]. Fortunately, milestones of deep learning and artificial intelligence have lead several researchers to create fast and accurate models for heart disease detection [1].
One of the ways to do this is through the use of previously-available digitized electrocardiogram (ECG/EKG) data and apply deep learning methods to classify specific heart disease [5]. With the already available dataset and publicly available paper tackling heart disease detection, the author decided to implement a deep learning model that would attain the same level of reported accuracy if not more using a state-of-the-art deep learning framework.
- The notebook that you may see here came from Kaggle workspace. Kaggle has the functionality to create a new notebook (kernel) based on an existing dataset in their platform. The author would suggest to create a new notebook by clicking the New Notebook button in this page seen below.
- To reproduce the notebook, copy the cells inside the ecg-heartbeat-categorization-prediction-project.ipynb to your newly-created kernel. Or if you want, you may copy and run all cels from this notebook.
- Ahuja A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7, e7702. https://doi.org/10.7717/peerj.7702
- Aljanabi, Maryam & Qutqut, Mahmoud & Hijjawi, Mohammad. (2018). Machine Learning Classification Techniques for Heart Disease Prediction: A Review. International Journal of Engineering and Technology. 7. 5373-5379. 10.14419/ijet.v7i4.28646.
- Heron, M. Deaths: Leading causes for 2017 [PDF – 3 M]. National Vital Statistics Reports;68(6). Accessed November 19, 2019.
- Fryar CD, Chen T-C, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999–2010 [PDF-494K]. NCHS data brief, no. 103. Hyattsville, MD: National Center for Health Statistics; 2012. Accessed May 9, 2019.
- Kachuee, Mohammad, Shayan Fazeli, and Majid Sarrafzadeh. “ECG Heartbeat Classification: A Deep Transferable Representation.” 2018 IEEE International Conference on Healthcare Informatics (ICHI) (2018): n. pag. Crossref. Web.