Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment

Casalino, Gabriella; Castellano, Giovanna; Kaymak, Uzay; Zaza, Gianluca

doi:10.1109/ssci50451.2021.9660104

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…Another interesting and recent approach is presented in [ 79 ]. More specifically, a neuro-fuzzy (NF) decision support system (namely, a combination of neural networks with fuzzy logic [ 80 ]) is proposed for learning predictive models in form of fuzzy rules, to assess cardiovascular risk.…”

Section: Discussionmentioning

confidence: 99%

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

Δρίτσας

Trigka

2023

Sensors

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Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique’s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%.

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Section: Discussionmentioning

confidence: 99%

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

Δρίτσας

Trigka

2023

Sensors

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“…The reason for this is climate change [ 50 ], which is increasingly affecting agricultural production. Based on these reasons, future research should consider using an adaptive network-based fuzzy inference system (ANFIS) since algorithms applying this system have proven to be efficient in decision support [ 51 ]. Additionally, this model reduces the possible decision-making errors made by experts.…”

Section: Discussionmentioning

confidence: 99%

Selection of an Insurance Company in Agriculture through Hybrid Multi-Criteria Decision-Making

Puška

Lukić²,

Božanić

et al. 2023

Entropy

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Crop insurance is used to reduce risk in agriculture. This research is focused on selecting an insurance company that provides the best policy conditions for crop insurance. A total of five insurance companies that provide crop insurance services in the Republic of Serbia were selected. To choose the insurance company that provides the best policy conditions for farmers, expert opinions were solicited. In addition, fuzzy methods were used to assess the weights of the various criteria and to evaluate insurance companies. The weight of each criterion was determined using a combined approach based on fuzzy LMAW (the logarithm methodology of additive weights) and entropy methods. Fuzzy LMAW was used to determine the weights subjectively through expert ratings, while fuzzy entropy was used to determine the weights objectively. The results of these methods showed that the price criterion received the highest weight. The selection of the insurance company was made using the fuzzy CRADIS (compromise ranking of alternatives, from distance to ideal solution) method. The results of this method showed that the insurance company DDOR offers the best conditions for crop insurance for farmers. These results were confirmed by a validation of the results and sensitivity analysis. Based on all of this, it was shown that fuzzy methods can be used in the selection of insurance companies.

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“…In [20], neuro-fuzzy systems were used to learn predictive models from training data to create decision rules meant to support the decision-making process in cardiovascular risk assessment. The reported accuracy has reached 0.91, proving that artificial intelligence models are a valuable help for clinicians.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

et al. 2023

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Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.

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Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment

Cited by 15 publications

References 35 publications

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

Selection of an Insurance Company in Agriculture through Hybrid Multi-Criteria Decision-Making

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

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