An Efficient Machine Learning-Based Decision-Level Fusion Model to Predict Cardiovascular Disease

Kibria, Hafsa Binte; Matin, Abdul

doi:10.1007/978-3-030-68154-8_92

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The class imbalance is handled using the random over sampler method. Kibria et al [8,10] proposed a decision-level fusion approach for the classification of heart disease. They fused two ML approaches to produce the best result.…”

Section: Review Of Literaturementioning

confidence: 99%

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Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Selvarathi¹,

Varadhaganapathy²

2023

ITC

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Type 2 Diabetes Mellitus (T2DM) is a common chronic disease that is caused due to insulin discharge disorder. Due to the complication of T2DM, the outcomes of this disease lead to severe illness, death and cardiovascular disease (CVD). Given a larger number of diabetes patients, it is necessary to find the patients with a high risk of CVD complications. For this, the traditional methods are not sufficient and it is important to develop a deep learning-based efficient quantitative model to predict the risk of CVD among diabetes patients. The major objective of this research is to assess the efficient artificial intelligence approach toward the proposal of a personalized deep learning model that can able to predict the risk of fatal and non-fatal CVD among T2DM patients. First, the unbalanced dataset is preprocessed to make the dataset balanced for processing. Second, the features are reduced and important features are selected using Rank based Feature Importance (RFI) model which will improve the prediction accuracy. Third, the proposed Cascaded Convolution Graph LSTM (CCGLSTM) has been used as a classifier to predict the risk of CVD. Novelty of the work resides on ranking based feature analysis is cascaded with CGLSTM. The proposed model is implemented and experimented with various evaluation metrics using the data from 560 patients of five-year follow-up with T2DM. These evaluated results are compared with the state of-the-art methods and the proposed model is proven to be superior to other approaches in terms of AUC (0.989), Accuracy (98.8%), recall (96.7%), precision (96.8%), specificity (97.4%) and F1-Score (97.5%).

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Section: Review Of Literaturementioning

confidence: 99%

“…In forward CGLSTM, the gate such as input gate it, temporal forget gate ft, hierarchical forget gate ht, control gate ct and output gate are updated using Eqn (8)(9)(10)(11)(12).…”

Section: Convolution Graph Lstm (Ccglstm)mentioning

confidence: 99%

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Selvarathi¹,

Varadhaganapathy²

2023

ITC

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“…The ensemble models used in [ 10 , 18 , 19 , 20 ], showed a better performance than any single ML algorithm, that is why the usage of ensemble models to diagnose disease has increased. In [ 21 ], the researchers built a decision-level fusion model to predict heart disease, which was further improved by applying the weighted score fusion [ 7 ]. The ensemble method was used in the diabetes detection and found promising results.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Kibria

Nahiduzzaman

Goni

et al. 2022

Sensors

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Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

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Ensemble Deep Learning Models for Accurate Prediction of Cardiovascular Disease Risk: A Comparative Analysis

Midhun,

Arun Raj,

Beereddy

et al. 2023

2023 2nd International Conference on Edge Computing and Applications (ICECAA)

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An Efficient Machine Learning-Based Decision-Level Fusion Model to Predict Cardiovascular Disease

Cited by 3 publications

References 10 publications

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Ensemble Deep Learning Models for Accurate Prediction of Cardiovascular Disease Risk: A Comparative Analysis

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