TL;DR
A stacking framework that uses harmony search optimization (HSO) to predict early cardiac disease in the course of the disease diagnosis process and the overall accuracy attained is 87%, multilayer stacking is 89%, and the stacking model employing HSO is 92%.
The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.
Nowadays, heart diseases are gaining international attention because they are regarded as a potentially fatal epidemic disease with difficult infection control around the world. Large number of models are proposed in the past. Among them machine learning gained more attention in heart disease prediction. Machine learning (ML) is an intelligent technique that can predict events with reasonable accuracy based on prior experience and learning. Meanwhile, a large number of ML models have been proposed to predict disease cases. As a result, the main challenge of this study requires an evaluation and benchmarking of ML models. Furthermore, no single study has addressed the issue of diagnosis model evaluation and benchmarking. This study, on the other hand, proposed an intelligent methodology to assist health organizations in the selection of heart disease diagnosis system. Benchmarking and evaluating diagnostic models for disease is a time-consuming process. There are numerous criteria to evaluate, some of which are in conflict with one another. Our research is organised as a decision matrix (DM) with ten evaluation criteria and twelve diagnostic models. The multi-criteria decision-making (MCDM) method is used to evaluate and benchmark the various chronic disease diagnostic models in terms of the evaluation criteria. TOPSIS-VIKOR is used for benchmarking and ranking, while Entropy is used to calculate the weights of criteria in an integrated MCDM method. The study's findings revealed that the benchmarking and selection issues associated with heart disease diagnosis models can be effectively addressed by combining Entropy and TOPSIS-VIKOR. Six Machine Learning Algorithms such as Naive Bayes, Artificial Neural Network, SVM, kNN, Logistic Regression, and Decision Tree are discussed for comparative analysis. To evaluate each of the methods used in this study, parameters such as accuracy, F1 score, Recall, Precision are used.The ML algorithms achieve accuracy of 0.862 in Naïve Bayes0.83 in K-Nearest Neighbor, 0.872 in Artificial Neural Network, 0.856 in Support Vector Machine, 0.756 in Decision Tree, 0.79 in Logistic Regression. From the results it is shown that the Artificial Neural Network classifier was chosen as the best diagnosis model for heart disease
Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In medical decision support systems, false negatives are more harmful.This paper presents a light-weight and interpretable machine learning approach for the early risk prediction of heart disease based on structured clinical data. Various models such as Logistic Regression, Random Forest, XGBoost, and stacking ensemble classifiers are compared based on clinically meaningful evaluation metrics such as accuracy, pre- cision, recall, F1-score, and ROC- AUC. The experimental results indicate that ensemble classifiers perform better than individual models, and the unoptimized StackingClassifier performs the best (Recall: 0.8807, F1-score: 0.8930, AUC: 0.9147). Cost-sensitive and threshold-optimized stacking further enhances the recall to 0.9266. To improve the transparency and clinical trust, SHAP and LIME are combined to offer global and local explanations. The findings point out ST depression, maximum heart rate reached, type of chest pain, cholesterol, and exercise-induced angina as the important risk factors. The proposed approach shows that simple and interpretable ensemble models can provide accurate heart disease risk predictions.
Heart disease affects millions of lives every year and stands as one of the most common and life-threatening health issues faced by people around the world. Detecting it early is essential to prevent associated outcomes, and machine learning is playing a key role in making this possible. Machine learning has transformed many industries by enabling systems to analyze data, recognize patterns, and make informed predictions. In healthcare, these capabilities are being used to develop models that help diagnose diseases such as heart disease.This study focuses on comparing and evaluating five ML algorithms: RegressionByDiscretization, M5P, lazy.KStar, DecisionTable, and M5Rules for the classification of heart disease using the Weka platform. Instead of emphasizing the prediction of heart failure, the focus is on analyzing and comparing how well these models perform in processing patient data and identifying trends. The experiments utilized the UCI Heart Disease dataset comprising 918 patient records with clinical features including age, chest pain type, cholesterol levels, resting ECG, and maximum heart rate. Each model was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Relative Root Squared Error (RRSE), and Correlation Coefficient. Among all the evaluated models, the \textit{Regression by Discretization} approach exhibited the most accurate predictions, achieving the lowest MAE of 0.0999 and RMSE of 0.1016, with RAE and RRSE values of 20.69\% and 20.95\% respectively, and an exceptionally high correlation of 0.9988. By evaluating multiple techniques, this research highlights the strengths and limitations of each approach, offering insights into which models may be more effective for similar healthcare applications. The findings underline the importance of choosing the right model and suggest that even greater accuracy may be achieved by exploring more advanced techniques in the future.
Liver disease is a serious health problem prevalent in the world. Early diagnosis is very important for enhancing quality of patient's health and reducing mortality. Traditional diagnostic techniques tend to take quite long time, be expensive and rely heavily on the skill of the medical personnel. That's why intelligence systems are needed that can make decisions based on data. This study suggests effective framework based on machine learning which allow to make accurate prediction about the liver disease. The methodology includes data preprocessing, which involves such steps as encoding categorical data, filtering out irrelevant information, as well as dealing with missing data. Then Random Forest and Recursive Feature Elimination can be used for feature selection. We proposed a framework to evaluate the performance of various machine learning classifiers such as Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP) and many ensemble learning models including Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost). To increase the prediction accuracy, we have developed a two-level ensemble stacking model with multiple ensemble classifiers as base learners and an advanced meta-learner to make predictions. The models have been assessed with accuracy, precision, recall, specificity, F1 score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), confusion matrix and Cross validation. The experiments conducted showed that the proposed two-level ensemble stacking framework is better compared to both individual classifiers as well as conventional ensemble techniques since it attained an accuracy close to 94%.
Healthcare is a major concern, among which heart disease is considered as one of the most important diseases where community has a big concern, when it is matter of accounting the ratio of global mortality rate along with morbidity. As the people are more aware now, and there is also easy availability of data sets, there is possibility of the acceptance of machine learning (ML) methods that make improvements in computational intelligence and have enhanced complete diagnosis of cardiovascular disease prediction and solution. At the current time, the beginning of Explainable Artificial Intelligence (XAI) has solved a problem of limitation that conventional system has of black-box modelling by permitting transparency and interpretability in system that make clinical decision power strong. As healthcare applications demand both predictive accuracy and trustworthiness, the integration of ML and XAI has become an important area of research in intelligent cardiovascular care. This systematic review examines contemporary machine learning methods and explainable AI procedures engaged prediction of heart diseases and classification. By collecting databases from various scientific sources and also ensuing a well-structured examination and while keeping in mind following also all the protocol, deep study is performed using these datasets. Also, all the strategies that must be followed during pre-processing step also prepared along with the approaches that will be applied during feature selection. All the required classification algorithms will also be identified with the calculation for metrics and work on explainability methods will be performed in order to identify the gap in the previous work. Also, emphasis will be also put on use of traditional machine learning methods, deep learning approaches and ensemble learning methods, along with post-hoc explanation that are human-understandable for understanding complex AI models. Some examples of these explainability models are SHAP, Saliency Maps, LIME, Integrated Gradients, attention-based interpretability mechanisms, Grad-CAM etc. This study also reviews and work on strengths, restrictions, gaps and practical consequences of already researched applications that present in real-world for medical diagnosis for better healthcare environments. But this becomes now mandatory to identify main research challenges that researchers are facing due to heterogeneity nature of data, privacy and security issues, imbalance of class, model generalizability, adoption by clinical practitioners, interpretability-performance trade-offs etc. All knowledge can be only gained after studying old research papers and do findings, so this research paper talks about all new developing trends and discuss future probable and research directions for emerging system with more transparency, better reliability, that must focus to benefit patients for the prediction of cardiovascular systems for medical diagnosis. The main aim in this paper is to give platform to the researchers, clinical practitioners and healthcare professionals for knowing the current scenario of cardiovascular disease prediction using machine learning approaches and also by applying explainability AI heart disease prediction models that will ease the process and also make the upcoming system with better progress and more trustworthy, operative and unfailing clinical solutions that makes system decision support one.
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