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Predicting heart disease is regarded as one of the most difficult challenges in the health-care profession. To predict cardiac disease, researchers employed a variety of algorithms including LDA, RF, GBC, DT, SVM, and KNN, as well as the feature selection algorithm sequential feature selection. For verification, the system employs the K-fold cross-...
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Coronary heart disease (CHD) is a leading cause of death globally, with over 382,000 deaths in the USA alone in 2020. The early detection of CHD is critical in reducing mortality rates. Artificial intelligence (AI) is a constantly evolving field of computer science that employs computational models to extract insights from past data and provide rap...
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One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One datas...
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... Therefore, any improvement that a physician can make during the diagnostic process may be crucial and necessary to greatly improve treatment results, because early diagnosis improves treatment results, as it improves the result of the procedure [6,7,8]. Currently, computer algorithms are specially relevant, since they can be used as an additional tool for decision making, known as Clinical Decision Support System (CDSS) [9,10]. It is precisely in the current paradigm of health information science where these CDSSs can be used to process in real time large amounts of data [11], using the available information to ease the physician's diagnostic process. ...
Background and Objective: Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation.
Methods: The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system.
Results: We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain \glspl{mr}, achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies.
Conclusions: This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.
... The methodology generates artificial instances of underrepresented categories, achieving a balanced distribution across both types. The process of classification involves the use of chosen characteristics and using various classifiers such as support vector machine (SVM) 21 , PCA 22 , linear discriminant analysis (LDA) 23 , naïve Bayes (NB) 24 , decision tree (DT) 25 , and random forest (RF) 26 . Ultimately, the classifier predicts an individual's presence or absence of heart disease. ...
Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model’s robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model’s predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system’s sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes.
... To develop an intelligent model using machine learning techniques to accurately forecast the first phases of liver disease, the authors of [24][25][26] developed a machine learning model to accurately forecast the occurrence of early liver disease by using a diverse range of features. The article describes the feature selection technique, the machine learning algorithm used, and the dataset utilized. ...
... We also have applied the Hyperparameter for finding the where; for measuring the performance we applied Optimizer, rate of learning, size of each batch, total epochs, CNN architecture, LSTM units, dropout rate of CNN and LSTM, and loss function. The data were gathered from 30,000 individuals, and experts were notified of the information [26]. The information collected from the Kaggle database was utilized to train DL systems to identify individuals with liver disease. ...
The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.
... In 2022, Ghulab Nabi Ahmad et al. [23] proposed to forecast cardiac disease utilising a number of algorithms like KNN, SVM, and sequential feature selection method. The system relies on K-fold validation for verification. ...
Globally, cardiovascular diseases (CVDs) rank among the leading causes of mortality. One out of every three deaths is attributed to cardiovascular disease, according to new World Heart Federation research. Cardiovascular disease can be caused by a number of factors, including stress, alcohol, smoking, a poor diet, inactivity, and other medical disorders like high blood pressure or diabetes. In contrast, for the vast majority of heart disorders, early diagnosis of associated ailments results in permanent recovery. Using newly developed data analysis technology, examining a patient's medical record could aid in the early detection of cardiovascular disease. Recent work has employed machine learning algorithms to predict cardiovascular illness on clinical datasets. However, because of their enormous dimension and class imbalance, clinical datasets present serious issues. An inventive model is offered in this work for addressing these problems.
An efficient decision support system, also known as an assistive system, is proposed in this paper for the diagnosis and classification of cardiovascular disorders. It makes use of an optimisation technique and a deep learning classifier. The efficacy of traditional techniques for predicting cardiovascular disease using medical data is anticipated to advance with the combination of the two methodologies. Deep learning systems can reduce mortality rates by predicting cardiovascular illness based on clinical data and the patient's severity level. For an adequate sample size of synthesized samples, the optimisation process chooses the right parameters to yield the best prediction from an enhanced classifier. The 99.58% accuracy was obtained by the proposed method. Also, PSNR, sensitivity, specificity, and other metrics were calculated in this work and compared with systems that are currently in use.
... The ability to predict the existence of HD is critical for administering the necessary therapy on time. The incidence of HD is predicted to quadruple by the year 2020, and it is expected that in 2050, one person will develop the disease every 30 s [4]. The symptoms and occurrence of heart disease vary according to the lifestyle of humans. ...
... It was projected in the year 2000 that adult Indians had lost a total of 9.2 billion years of productive life, which contributed to the overall economic loss. According to research conducted in 2015, the average lifespan of an Indian man is estimated to be 67.3 years, whilst the average lifespan of an Indian woman is estimated to be 69.9 years [4]. It was projected in the year 2000 that adult Indians had lost a total of 9.2 billion years of productive life, which contributed to the overall economic loss. ...
This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on "Early Heart Disease Prediction using Boosting Techniques", this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost's superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction.
... Ahmad et al. [28] conducted a comparative investigation on the optimal medical diagnosis of human heart disease using machine learning techniques. They specifically examined the impact of sequential feature selection, comparing its inclusion with conventional machine learning approaches that do not employ this feature selection method. ...
... In this section, we conduct a comparative analysis of the EHMFFL algorithm against three existing heart disease diagnosis methods. Since the EHMFFL algorithm utilizes a heuristic-metaheuristic-driven feature selection algorithm (i.e., PCC-GWO) embedded in an ensemble learning model, we considered the machine learning approach based on LR by Jindal et al. (2021) [18] (referred to as ML), an ensemble of different machine learning models, including DT, RF, SVM, KNNs, and ANN, developed by Shorewala (2021) [23] (referred to as EL), and a sequential heuristic feature selection method by Ahmad et al. (2022) [28] (referred to as FS). We assess the precision, recall, and accuracy of the different methods, as depicted in Figures 12 and 13, for the Cleveland and Statlog datasets, respectively. ...
... In this section, we conduct a comparative analysis of the EHMFFL algorithm against three existing heart disease diagnosis methods. Since the EHMFFL algorithm utilizes a heuristic-metaheuristic-driven feature selection algorithm (i.e., PCC-GWO) embedded in an ensemble learning model, we considered the machine learning approach based on LR by Jindal et al. (2021) [18] (referred to as ML), an ensemble of different machine learning models, including DT, RF, SVM, KNNs, and ANN, developed by Shorewala (2021) [23] (referred to as EL), and a sequential heuristic feature selection method by Ahmad et al. (2022) [28] (referred to as FS). We assess the precision, recall, and accuracy of the different methods, as depicted in Figures 12 and 13, for the Cleveland and Statlog datasets, respectively. ...
Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an ensemble heuristic-metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost techniques. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic-metaheuristic approach that integrates the heuristic knowledge of the Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets, yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.
... Ahmad et al. [66] studied the performance of several algorithms using different heart disease datasets, including Cleveland, Switzerland, and Long Beach. The algorithms studied include random forest, decision tree, support vector machine (SVM), k-nearest neighbor (KNN), linear discriminant analysis, and gradient boosting classifier. ...
... Decision tree-based methods have also been employed to diagnose COVID-19. Yoo et al. [66] proposed a deep learning-based decision tree model to detect COVID-19 using chest X-ray images. The approach consists of three decision trees trained using deep learning architectures, including a convolutional neural network (CNN). ...
Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant popularity among the diverse range of ML algorithms due to their simplicity and interpretability. This paper presents a comprehensive overview of decision trees, including the core concepts, algorithms, applications, their early development to the recent high-performing ensemble algorithms and their mathematical and algorithmic representations, which are lacking in the literature and will be beneficial to ML researchers and industry experts. Some of the algorithms include classification and regression tree (CART), Iterative Dichotomiser 3 (ID3), C4.5, C5.0, Chi-squared Automatic Interaction Detection (CHAID), conditional inference trees, and other tree-based ensemble algorithms, such as random forest, gradient-boosted decision trees, and rotation forest. Their utilisation in recent literature is also discussed, focusing on applications in medical diagnosis and fraud detection.
... The ability to predict the existence of HD is critical for administering the necessary therapy on time. The incidence of HD is predicted to quadruple by the year 2020, and it is expected that in 2050, one person will develop the disease every 30 seconds [4]. The symptoms and occurrence of heart disease vary according to the lifestyle of humans. ...
... It was projected in the year 2000 that adult Indians had lost a total of 9.2 billion years of productive life, which contributed to the overall economic loss. According to research conducted in 2015, the average lifespan of an Indian man is estimated to be 67.3 years, whilst the average lifespan of an Indian woman is estimated to be 69.9 years [4]. It was projected in the year 2000 that adult Indians had lost a total of 9.2 billion years of productive life, which 3 of 21 contributed to the overall economic loss. ...
The World Health Organization (WHO) has released reports indicating that heart disorders hold the unfortunate distinction of being the primary cause of death worldwide. Shockingly, an
astonishing estimated 17.9 million lives are claimed by heart diseases annually, accounting for an alarming 31% of all global deaths. With all the flaws in the clinical situation, it is frequently
challenging to assess the severity of cardiac disease and forecast its course of progression due to heterogeneity and complex interplay of factors. Therefore, early heart disease detection is essential for effective therapy. To address these challenges, Machine Learning (ML) boosting algorithms play a pivotal role as the main components of predictive analytics required to do this. The main objective of this study is to develop a comprehensive comparative framework to predict heart diseases using state-of-the-art machine learning with boosting techniques such as Decision Tree, Random Forest, Gradient Boosting, Catboost, XGboost, Light GBM, and Adaboost. To evaluate the performance
of the models, a large heart disorders dataset is used from the UCI machine learning repository, comprised of 26 feature-based numerical and categorical attributes with 8763 samples from all over the globe. The Experimental results reveal that AdaBoost attained the highest accuracy of 95% and outperforms other algorithms concerning various performance measures like precision= 0.98, recall=0.95, specificity= 0.95, f1-score=0.01, Negative predicted value= 0.83, False positive rate= 0.04, False
negative rate= 0.04 and False Development rate= 0.01
... It aims to create a framework for predicting disease occurrences, improving heart disease analysis, and enhancing decision support systems. [4] 1) Advantages High accuracy: Random Forest Classifier and Decision Tree Classifier achieved 100% and 99.76% accuracy, respectively, for specific datasets. Framework creation: Aims to establish a framework for predicting disease occurrences, enhancing heart disease analysis, and improving decision support systems. ...
In this examine, we awareness on cardiovascular disease, a major worldwide motive of mortality. Researchers use gadget getting to know and records evaluation strategies to enhance the prognosis of this ailment. We introduce a brand new version, the Quine McCluskey Binary Classifier (QMBC), which combines seven extraordinary fashions to efficiently become aware of patients with coronary heart disease. To decorate performance, we appoint feature selection and extraction methods.First, we discover the top 10 relevant features from the dataset the use of Chi-rectangular and ANOVA approaches. We then lessen the dimensionality of the facts with principal aspect analysis, retaining nine essential additives. The QMBC version combines the outputs of the seven fashions to create a truthful rule for predicting coronary heart ailment. The outcomes from the seven fashions are dealt with as unbiased functions, while the target attribute depends on those results. Our proposed QMBC version outperforms present methods, establishing its effectiveness in heart disorder prediction.
... By selecting and assigning weights to relevant features, the research aims to improve the efficiency and precision of predictive models in diagnosing heart disease. 5 Ahmad et al. [28] conducted a comparative investigation on the optimal medical diagnosis of human heart disease using machine learning techniques. They specifically examined the impact of sequential feature selection, comparing its inclusion with conventional machine learning approaches that do not employ this feature selection method. ...
... In this section, we conduct a comparative analysis of the EHMFFL algorithm against three other heart disease diagnosis techniques. These techniques include a machine learning approach by Jindal et al. (2021) [18] (referred to as ML), an ensemble learning model developed by Shorewala (2021) [23] (referred to as EL), and a feature selection method by Ahmed et al. (2022) [28] (referred to as FS). We assess the precision, recall, and accuracy achieved by these different approaches, as depicted in Figures 12 and 13 for the Cleveland and Statlog datasets, respectively. ...
Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in pre-venting adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an Ensemble Heuristic-Metaheuristic Feature Fusion Learning (EHMFFL) algorithm for heart disease diagnosis. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic-metaheuristic approach that integrates the heuristic knowledge of Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning, aimed at constructing a robust prediction model. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art machine learning, ensemble learning, and feature selection techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.
