An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases
نویسندگان
چکیده
Cardiovascular diseases are considered as the most life-threatening syndromes with highest mortality rate globally. Over a period of time, they have become very common and now overstretching healthcare systems countries. The major factors cardiovascular high blood pressure, family history, stress, age, gender, cholesterol, Body Mass Index (BMI), unhealthy lifestyle. Based on these factors, researchers proposed various approaches for early diagnosis. However, accuracy techniques needs certain improvements due to inherent criticality life threatening risks diseases. In this article, MaLCaDD (Machine Learning based Disease Diagnosis) framework is effective prediction precision. Particularly, first deals missing values (via mean replacement technique) data imbalance Synthetic Minority Over-sampling Technique - SMOTE). Subsequently, Feature Importance technique utilized feature selection. Finally, an ensemble Logistic Regression K-Nearest Neighbor (KNN) classifiers higher accuracy. validation performed through three benchmark datasets (i.e. Framingham, Heart Cleveland) accuracies 99.1%, 98.0% 95.5 % achieved respectively. comparative analysis proves that predictions more accurate (with reduced set features) compared existing state-of-the-art approaches. Therefore, highly reliable can be applied in real environment diagnosis
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3098688