In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier life for patients. Researchers have used traditional machine learning (ML) techniques for CVD prediction and classification. However, many of them are inaccurate and time-consuming due to the unavailability of quality data including imbalanced samples, inefficient data preprocessing, and the existing selection criteria. These factors lead to an overfitting or bias issue towards a certain class label in the prediction model. Therefore, an intelligent system is needed which can accurately diagnose CVDs. We proposed an automated ML model for various kinds of CVD prediction and classification. Our prediction model consists of multiple steps. Firstly, a benchmark dataset is preprocessed using filter techniques. Secondly, a novel arithmetic optimization algorithm is implemented as a feature selection technique to select the best subset of features that influence the accuracy of the prediction model. Thirdly, a classification task is implemented using a multilayer perceptron neural network to classify the instances of the dataset into two class labels, determining whether they have a CVD or not. The proposed ML model is trained on the preprocessed data and then tested and validated. Furthermore, for the comparative analysis of the model, various performance evaluation metrics are calculated including overall accuracy, precision, recall, and F1-score. As a result, it has been observed that the proposed prediction model can achieve 88.89% accuracy, which is the highest in a comparison with the traditional ML techniques.

These days, intelligent cybersecurity models based on machine learning and data mining techniques are prevalent. Several factors might affect the quality of these models, including the accuracy, the ability to train new models quickly, the quick decision-making process, the simplicity of the created models, and the model’s interpretability. Feature selection algorithms can help achieve all these characteristics by isolating the crucial features from the unimportant ones during the model creation phase. The current article proposes an intelligent intrusion detection model based on an improved cuckoo search algorithm which is a nature-inspired optimization algorithm. The improved cuckoo search algorithm proposed in this article may tolerate several bad steps toward determining the set of effective features that would preserve or maximize the capabilities of the produced classification models. The generalization ability of such an algorithm is examined by applying it to 10 benchmark datasets, and it showed superior outcomes compared with several nature-inspired attribute selection approaches. Later, the improved cuckoo search algorithm is used to develop intelligent intrusion detection systems using the well-known “NSL-KDD” dataset. The obtained outcomes are appealing regarding the general performance, the time required to develop the intelligent intrusion detection models, and the number of rules generated.

Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.