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Implementation of Artificial Intelligence Models for Enhanced Cardiovascular Disease Prediction and Risk Assessments
Cardiovascular disease (CVD) also known as heart disease is one of the most common causes of death globally, accounting for over 17 million deaths per year, which represents 31% of all deaths worldwide. Therefore, the prediction of CVD values is a crucial aspect of healthcare and disease management. This study aims to predict CVD values using three different models; Multiple linear regression (MLR), Artificial neural network (ANN), and Adaptive neuro-fuzzy inference systems (ANFIS). Twelve independent variables were used in training the models. The individual performance of the models was evaluated using four different performance objectives; Root Mean squared error (RMSE), Mean squared error (MSE), correlation coefficient (R), and determination coefficient (R2). The results indicated that the AI-based techniques depict the best prediction performance with ANFIS having R2 = 0.99, R = 0.99, RMSE = 0.068, and MSE = 0.0046 respectively. In general, the ANFIS model was found to be the most reliable for predicting CVD values.
Implementation of Artificial Intelligence Models for Enhanced Cardiovascular Disease Prediction and Risk Assessments
Cardiovascular disease (CVD) also known as heart disease is one of the most common causes of death globally, accounting for over 17 million deaths per year, which represents 31% of all deaths worldwide. Therefore, the prediction of CVD values is a crucial aspect of healthcare and disease management. This study aims to predict CVD values using three different models; Multiple linear regression (MLR), Artificial neural network (ANN), and Adaptive neuro-fuzzy inference systems (ANFIS). Twelve independent variables were used in training the models. The individual performance of the models was evaluated using four different performance objectives; Root Mean squared error (RMSE), Mean squared error (MSE), correlation coefficient (R), and determination coefficient (R2). The results indicated that the AI-based techniques depict the best prediction performance with ANFIS having R2 = 0.99, R = 0.99, RMSE = 0.068, and MSE = 0.0046 respectively. In general, the ANFIS model was found to be the most reliable for predicting CVD values.
Implementation of Artificial Intelligence Models for Enhanced Cardiovascular Disease Prediction and Risk Assessments
Ozsahin, Dilber Uzun (author) / Onakpojeruo, Efe Precious (author) / Duwa, Basil Bartholomew (author) / Uzun, Berna (author) / Zira, Yoshebel Francis (author) / Ozsahin, Ilker (author)
2024-06-03
587105 byte
Conference paper
Electronic Resource
English
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