In the modern world, there have been some revolutionary advancement in the field of medical science and research and this is no different for electrocardiogram. Electrocardiogram (also abbreviated as ECG) illustrates the electrical activity of one’s heart over a time period. Over the years, number of people suffering from heart disease have increased to some extent. Therefore, in our research, we aim to design a model using supervised machine learning that can find anomalies in one’s ECG report by analyzing it. We have applied six supervised machine learning algorithms to distinguish between normal and abnormal ECG. In addition, we used them to predict the chances of a patient suffering from a certain heart disease. We divided our data set into two parts. 75% data in one group for training the model and rest 25% data in another group for testing. To avoid any kind of anomalies or repetitions, Cross Validation and Random Train-Test Split was used to obtain an answer as accurate as possible. We have compared the results with each other for a better understanding.
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ECG data analysis and heart disease prediction using machine learning algorithms
Semantic Scholar · Medicine · 2019
Abstract
In the modern world, there have been some revolutionary advancement in the field of medical science and research and this is no different for electrocardiogram. Electrocardiogram (also abbreviated as ECG) illustrates the electrical activity of one’s heart over a time period. Over the years, number of people suffering from heart disease have increased to some extent. Therefore, in our research, we aim to design a model using supervised machine learning that can find anomalies in one’s ECG report by analyzing it. We have applied six supervised machine learning algorithms to distinguish between normal and abnormal ECG. In addition, we used them to predict the chances of a patient suffering from a certain heart disease. We divided our data set into two parts. 75% data in one group for training the model and rest 25% data in another group for testing. To avoid any kind of anomalies or repetitions, Cross Validation and Random Train-Test Split was used to obtain an answer as accurate as possible. We have compared the results with each other for a better understanding.