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An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease
Farhat Ullah1, Xin Chen1, Khairan Rajab2
1School of Automation, China University of Geosciences, Wuhan 430074, China.
Insights
This study developed an accurate machine learning model for predicting heart disease, outperforming traditional methods. Feature reduction improved classifier efficiency and accuracy for reliable cardiac disease detection.
Area of Science:
- Cardiology
- Computer Science
- Data Science
Background:
- Coronary heart disease significantly impacts human life, necessitating reliable diagnostic tools.
- Traditional medical history-based diagnosis of heart disease is often unreliable.
- Machine learning (ML) offers a more accurate and efficient approach to cardiac disease detection.
Purpose of the Study:
- To address shortcomings in existing heart disease detection methods.
- To construct an accurate machine learning model for predicting heart disease.
- To evaluate the impact of feature selection on model performance and efficiency.
Main Methods:
- Utilized five feature selection algorithms to optimize a machine learning model.
- Evaluated model performance using metrics like accuracy, precision, recall, F1-score, and MCC.
- Assessed the impact of feature reduction on classifier performance and execution time.
Main Results:
- Support Vector Machine (SVM) achieved 97.5% accuracy.
- K-Nearest Neighbor (KNN) achieved 95% accuracy.
- Logistic Regression achieved 93% accuracy, with reduced computation times for all models.
Conclusions:
- Feature reduction positively impacts classifier performance and reduces computation time.
- The proposed ML model demonstrates high accuracy and efficiency in heart disease prediction.
- Accurate and precise heart disease detection using ML can help prevent human loss.
Abstract:
Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.
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