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Machine Learning Technology-Based Heart Disease Detection Models
Umarani Nagavelli1, Debabrata Samanta1,2, Partha Chakraborty3
1Dayananda Sagar Research Foundation, University of Mysore (UoM), Mysore, Karnataka, India.
Insights
This study explores machine learning models for early heart disease detection. XGBoost and other algorithms show promise in improving diagnostic accuracy for better patient outcomes.
Area of Science:
- Cardiology
- Computer Science
- Artificial Intelligence
Background:
- Heart failure disease is a significant global health concern.
- Early detection of heart disease is critical for effective healthcare services.
- Electrocardiogram (ECG) is a standard diagnostic tool, but advanced methods are needed.
Purpose of the Study:
- To analyze various machine learning technologies for heart disease detection.
- To provide clinicians with a tool for early heart problem diagnosis.
- To improve the accuracy of heart disease diagnosis using machine learning.
Main Methods:
- Utilized Naïve Bayes with a weighted approach for heart disease prediction.
- Employed Support Vector Machine (SVM) and XGBoost for ischemic heart disease detection based on frequency and time domain features.
- Developed a heart failure prediction model using DBSCAN for outlier detection, SMOTE-ENN for data balancing, and XGBoost for classification.
Main Results:
- Compared four machine learning models based on precision, accuracy, f1-measure, and recall.
- XGBoost demonstrated strong performance in classification tasks for heart disease prediction.
- The study investigated alternative decision tree classification algorithms to enhance diagnostic accuracy.
Conclusions:
- Machine learning offers valuable tools for disease diagnosis, detection, and prediction in the medical industry.
- Early diagnosis through improved prediction models can lead to more effective patient treatment and prevention of severe complications.
- The integration of advanced machine learning techniques can significantly aid clinical decision support systems.
Abstract:
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.
