Assam Don Bosco University, Guwahati, India.
South Eastern University of Sri Lanka, Oluvil, Sri Lanka.
Technol Health Care. 2024;32(6):4545-4569. doi: 10.3233/THC-240740.
Heart disease is a severe health issue that results in high fatality rates worldwide. Identifying cardiovascular diseases such as coronary artery disease (CAD) and heart attacks through repetitive clinical data analysis is a significant task. Detecting heart disease in its early stages can save lives. The most lethal cardiovascular condition is CAD, which develops over time due to plaque buildup in coronary arteries, causing incomplete blood flow obstruction. Machine Learning (ML) is progressively used in the medical sector to detect CAD disease.
The primary aim of this work is to deliver a state-of-the-art approach to enhancing CAD prediction accuracy by using a DL algorithm in a classification context.
A unique ML technique is proposed in this study to predict CAD disease accurately using a deep learning algorithm in a classification context. An ensemble voting classifier classification model is developed based on various methods such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Bidirectional LSTM and Long Short-Term Memory (LSTM). The performance of the ensemble models and a novel model are compared in this study. The Alizadeh Sani dataset, which consists of a random sample of 216 cases with CAD, is used in this study. Synthetic Minority Over Sampling Technique (SMOTE) is used to address the issue of imbalanced datasets, and the Chi-square test is used for feature selection optimization. Performance is assessed using various assessment methodologies, such as confusion matrix, accuracy, recall, precision, f1-score, and auc-roc.
When a novel algorithm achieves the highest accuracy relative to other algorithms, it demonstrates its effectiveness in several ways, including superior performance, robustness, generalization capability, efficiency, innovative approaches, and benchmarking against baselines. These characteristics collectively contribute to establishing the novel algorithm as a promising solution for addressing the target problem in machine learning and related fields.
Implementing the novel model in this study significantly improved performance, achieving a prediction accuracy rate of 92% in the detection of CAD. These findings are competitive and on par with the top outcomes among other methods.
心脏病是一种严重的健康问题，在全球范围内导致高死亡率。通过重复的临床数据分析来识别心血管疾病，如冠状动脉疾病（CAD）和心脏病发作，是一项重要的任务。早期发现心脏病可以挽救生命。最致命的心血管疾病是 CAD，它是由于冠状动脉斑块积聚导致的，随着时间的推移会导致不完全的血流阻塞。机器学习（ML）在医疗领域越来越多地被用于检测 CAD 疾病。
本工作的主要目的是在分类背景下使用 DL 算法提供一种用于提高 CAD 预测准确性的最新方法。
本研究提出了一种独特的 ML 技术，旨在使用深度学习算法在分类背景下准确预测 CAD 疾病。基于 Naive Bayes（NB）、Logistic Regression（LR）、Decision Tree（DT）、XGBoost、Random Forest（RF）、Convolutional Neural Network（CNN）、Support Vector Machine（SVM）、K Nearest Neighbor（KNN）、Bidirectional LSTM 和 Long Short-Term Memory（LSTM）等多种方法，开发了一个集成投票分类模型。在这项研究中，比较了集成模型和一个新模型的性能。该研究使用了由 216 例 CAD 随机样本组成的 Alizadeh Sani 数据集。使用合成少数过采样技术（SMOTE）来解决数据集不平衡的问题，并用卡方检验进行特征选择优化。使用多种评估方法，如混淆矩阵、准确性、召回率、精度、f1 分数和 auc-roc，来评估性能。
当一种新算法相对于其他算法获得最高精度时，它在多个方面表现出了有效性，包括优异的性能、鲁棒性、泛化能力、效率、创新方法以及与基准的比较。这些特性共同为新算法确立了在机器学习和相关领域解决目标问题的有前途的解决方案。
在这项研究中实施新模型显著提高了性能，在 CAD 检测中达到了 92%的预测准确率。这些发现具有竞争力，与其他方法的最佳结果相当。
