Scalable Prediction of Heart Disease using a Hybrid Model: A Machine Learning Perspective
DOI:
https://doi.org/10.26438/ijcse/v11i8.4047Keywords:
machine learning, heart disease, feature learningAbstract
"Scalable Prediction of Heart Disease using a Hybrid Model: A Machine Learning Perspective" presents a approach to predict heart disease using a hybrid machine learning model. The proposed model combines different machine learning algorithms to improve the prediction accuracy and scalability. The dataset used in the study contains various clinical and demographic features of patients, which were pre-processed and feature-selected to reduce noise and improve the model`s performance. Heart disease is a leading cause of mortality worldwide, and early diagnosis and treatment can significantly improve patient outcomes. Machine learning algorithms have shown promising results in predicting heart disease using clinical and demographic data. The performance of the model was evaluated using several evaluation metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The results show that the proposed hybrid model outperformed other state-of-the-art machine learning models in terms of prediction accuracy and scalability. The dataset was preprocessed and feature-selected to reduce noise and improve the model`s performance. The training process was parallelized using distributed computing to reduce the training time and improve the scalability of the model. the study provides a valuable contribution to the field of machine learning in healthcare and highlights the potential of using advanced algorithms to improve the diagnosis and treatment of cardiovascular diseases.
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