Cardiovascular disease is the primary reason for mortality worldwide, responsible for around a third of all deaths. To assist medical professionals in quickly identifying and diagnosing patients, numerous machine learning and data mining techniques are utilized to predict the disease. Many researchers have developed various models to boost the efficiency of these predictions. Feature selection and extraction techniques are utilized to remove unnecessary features from the dataset, thereby reducing computation time and increasing the efficiency of the models. In this study, we introduce a new ensemble Quine McCluskey Binary Classifier (QMBC) technique for identifying patients diagnosed with some form of heart disease and those who are not diagnosed. The QMBC model utilizes an ensemble of seven models, including logistic regression, decision tree, random forest, K-nearest neighbour, naive bayes, support vector machine, and multilayer perceptron, and performs exceptionally well on binary class datasets. We employ feature selection and feature extraction techniques to accelerate the prediction process. We utilize Chi-Square and ANOVA approaches to identify the top 10 features and create a subset of the dataset. We then apply Principal Component Analysis to the subset to identify 9 prime components. We utilize an ensemble of all seven models and the Quine McCluskey technique to obtain the Minimum Boolean expression for the target feature. The results of the seven models ( x ₀ , x ₁ , x ₂ , ..., x ₆ ) are considered independent features, while the target attribute is dependent. We combine the projected outcomes of the seven ML models and the target feature to form a foaming dataset. We apply the ensemble model to the dataset, utilizing the Quine McCluskey minimum Boolean equation built with an 80:20 train-to-test ratio. Our proposed QMBC model surpasses all current state-of-the-art models and previously suggested methods put forward by various researchers.

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time.

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree $\left(SA{C}_D\right)$, Similarity based on Average Betweenness $\left(SA{C}_B\right)$, Similarity based on Average Closeness $\left(SA{C}_C\right)$, and Similarity based on Average Clustering Coefficient $\left(SA{C}_{CC}\right)$. Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic–Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm $\left(CCPA\right)$, and keyword network link prediction $\left(KNLP\right)$. We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method.