Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

ROBUST DETECTION OF CORONARY HEART DISEASE USING MACHINE LEARNING ALGORITHMS

Predicting whether or not someone will get heart or cardiac disease is now one of the most difficult jobs in the area of medicine. Heart disease is responsible for the deaths of about one person per minute in the contemporary age. Processing the vast amounts of data that are generated in the field of healthcare is an important application for data science. Because predicting cardiac disease is a difficult undertaking, there is a pressing need to automate the prediction process to minimize the dangers that are connected with it and provide the patient with timely warning. The chapter one in this thesis report highlights the importance of this problem and identifies the need to augment the current technological efforts to produce relatively more accurate system in facilitating the timely decision about the problem. The chapter one also presents the current literature about the theories and systems developed and assessed in this direction.This thesis work makes use of the dataset on cardiac illness that can be found in the machine learning repository at UCI. Using a variety of data mining strategies, such as Naive Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest, the work that has been reported in this thesis estimates the likelihood that a patient would develop heart disease and can categorize the patient\u27s degree of risk. The performance of chosen classifiers is tested on chosen feature space with help of feature selection algorithm. On Cleveland heart datasets of heart disease, the models were placed for training and testing. To assess the usefulness and strength of each model, several performance metrics are utilized, including sensitivity, accuracy, AUC, specificity, ROC curve and F1-score. The effort behind this research leads to conduct a comparative analysis by computing the performance of several machine learning algorithms. The results of the experiment demonstrate that the Random Forest and Support Vector machine algorithms achieved the best level of accuracy (94.50% and 91.73% respectively) on selected feature space when compared to the other machine learning methods that were employed. Thus, these two classifiers turned out to be promising classifiers for heart disease prediction. The computational complexity of each classifier was also investigated. Based on the computational complexity and comparative experimental results, a robust heart disease prediction is proposed for an embedded platform, where benefits of multiple classifiers are accumulated. The system proposes that heart disease detection is possible with higher confidence if and only if many of these classifiers detect it. In the end, results of experimental work are concluded and possible future strategies in enhancing this effort are discussed

Enhancing coronary artery diseases screening:A comprehensive assessment of machine learning approaches using routine clinical and laboratory data

Introduction: Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data. Material and Methods: Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and stacked ensemble learning were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model. Results: The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold cross-validation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90.38%, 90.07%, and 90.39%, sensitivities of 94.43%, 93.03%, and 93.96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88.79%, 88.77%, and 90.06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model. Conclusion: The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the Z-Alizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings.</p

A Comparison of Supervised Machine Learning Algorithms on Heart Disease Risk Predictions

Cardiac disease detection and prediction have historically presented challenges for physicians, often requiring significant time and resources. Consequently, expensive therapies and operations are administered in hospitals and clinics to treat cardiac disorders. Therefore, early anticipation of cardiac disease holds immense potential in enabling individuals worldwide to seek timely treatment before the condition escalates. In recent years, heart disease has emerged as a prevalent global health issue, primarily attributed to excessive alcohol and tobacco use, as well as a lack of physical activity. This paper focuses on the utilization of machine learning methods to forecast cardiac illnesses. A comprehensive dataset encompassing diverse human health parameters is employed for training and testing purposes. Various AI and ML algorithms are implemented to predict cardiac disorders, and their performance is rigorously compared. The findings of this study contribute to the growing body of research on cardiac disease detection and prediction, highlighting the efficacy of machine learning approaches. By leveraging these algorithms, healthcare professionals can potentially identify individuals at risk of cardiac disease at an early stage, enabling proactive intervention and preventive measures. Moreover, the comparative analysis of multiple machine learning algorithms offers valuable insights into their respective strengths and limitations, aiding in the selection of the most suitable approach for specific clinical scenarios

Classification Model for Meticulous Presaging of Heart Disease Detection through SDA and NCA using Machine learning :CMSDANCA

For the design and implementation of CDSS, computation time and prognostic accuracy are very important. To analyze the large collection of a dataset for detecting and diagnosis disease ML techniques are used. According to the reports of World Health Organizations, HD is a major cause of death and killer in urban and rural areas or worldwide. The main reason for this is a shortage of doctors and delay in the diagnosis. In this research work, heart disease is a diagnosis by the data mining techniques and used the clinical parameters of patients for early stages diagnosis. The intend of this learning to develop a representation that relies on the prediction method for coronary heart disease. This proposed work used the approach of self-diagnosis Algorithm, Fuzzy Artificial neural network, and NCA &amp; PCA and imputation methods. By the use of this technique computation time for prediction of Coronary HD can be reduced. For the implementation of this the two datasets are using such as Cleveland and Statlog datasets that is collected from the UCI kaggle the ML repository. The datasets for the disease prediction measure are used to accurately calculate the difference between variables and to determine whether they are correlated or not. For this classification model, the performance measure is calculated in requisites of their accuracy, precision, recall, and specificity. This approach is evaluated on the heart disease datasets for improving the accuracy performance results obtained. The outcome for KNN+SDA+NCA+FuzzyANN for Cleveland dataset accuracy achieved 98.56 %.and for Statlog dataset 98.66 %.

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

An Empirical study on Predicting Blood Pressure using Classification and Regression Trees

Blood pressure diseases have become one of the major threats to human health. Continuous measurement of bloodpressure has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models with lowmeasurement accuracy or long training time, non-invasive blood pressure measurement is a promising use for continuousmeasurement. Thus in this paper, classification and regression trees (CART) are proposed and applied to tackle the problem. Firstly,according to the characteristics of different information, different CART models are constructed. Secondly, in order to avoid theover-fitting problem of these models, the cross-validation method is used for selecting the optimum parameters so as to achieve thebest generalization of these models. Based on the biological data collected from CM400 monitor, this approach has achieved betterperformance than the common existing models such as linear regression, ridge regression, the support vector machine and neuralnetwork in terms of accuracy rate, root mean square error, deviation rate, Theil IC, and the required training time is also comparativelyless. With increasing data, the accuracy rate of predicting systolic blood pressure and diastolic blood pressure by CART exceeds 90%,and the training time is less than 0.5s