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Heart disease is the most life-threatening disease globally, affecting human life very critically. On-time and precise diagnosis of heart disease is vital for the prevention and treatment of heart failure. In the earlier stage, several detection techniques were developed to detect heart disease using machine learning techniques. But the heart problems have not been detected accurately with the minimum required time. This paper presented an effective heart disease prediction system that accurately diagnoses abnormality within a short duration. The feature selection process is carried with stochastic gradient boosting with a recursive feature elimination approach. For classification, the features are clustered by using the adaptive Harris hawk optimization clustering approach. Based on clustered features, the classification is processed with an enhanced deep genetic algorithm. This method improves the performance of the deep neural network by augmenting its initial weights with an enhanced genetic algorithm, which recommends better weights for the neural network. The demonstration of the proposed work is simulated in the MATLAB platform and the dataset used in UCI machine learning repository. The experimental outcome shows that the developed feature selection and classification approach attain 98.36% and 97.3% accuracy compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

Imbalanced problems are quite pervasive in many real-world applications. In imbalanced distributions, a class or some classes of data, called minority class(es), is/are under-represented compared to other classes. This skewness in the data underlying distribution causes many difficulties for typical machine learning algorithms. The notion becomes even more complicated when machine learning algorithms are to combat multi-class imbalanced problems. The presented solutions for tackling the issues arising from imbalanced distributions, generally fall into two main categories: data-oriented methods and model-based algorithms. Focusing on the latter, this paper suggests an elegant blend of boosting and over-sampling paradigms, which is called MDOBoost, to bring considerable benefits to the learning ability of multi-class imbalanced data sets. The over-sampling technique introduced and adopted in this paper, Mahalanobis distance-based over-sampling technique (MDO in short), is delicately incorporated into boosting algorithm. In fact, the minority classes are over-sampled via MDO technique in such a way that they almost preserve the original minority class characteristics. MDO, in comparison with the popular method in this field, SMOTE, generates more similar minority class examples to original class samples. Moreover, the broader representation of minority class examples is provided via MDO, and this, in turn, causes the classifier to build larger decision regions. MDOBoost increases the generalization ability of a classifier, since it indicates better results with pruned version of C4.5 classifier; unlike other over-sampling/boosting procedures, which have difficulties with pruned version of C4.5. MDOBoost is applied to real-world multi-class imbalanced benchmarks and its performance is then compared with several data-level and model-based algorithms. The empirical results and theoretical analyses reveal that MDOBoost offers superior advantages compared to popular class decomposition and over-sampling techniques in terms of MAUC, G-mean, and minority class recall. [ABSTRACT FROM AUTHOR]

Instance selection is an important research problem of data pre-processing in the data mining field. The aim of instance selection is to reduce the data size by filtering out noisy data, which may degrade the mining performance, from a given dataset. Genetic algorithms have presented an effective instance selection approach to improve the performance of data mining algorithms. However, current approaches only pursue the simplest evolutionary process based on the most reasonable and simplest rules. In this paper, we introduce a novel instance selection algorithm, namely a genetic-based biological algorithm (GBA). GBA fits a 'biological evolution' into the evolutionary process, where the most streamlined process also complies with the reasonable rules. In other words, after long-term evolution, organisms find the most efficient way to allocate resources and evolve. Consequently, we can closely simulate the natural evolution of an algorithm, such that the algorithm will be both efficient and effective. Our experiments are based on comparing GBA with five state-of-the-art algorithms over 50 different domain datasets from the UCI Machine Learning Repository. The experimental results demonstrate that GBA outperforms these baselines, providing the lowest classification error rate and the least storage requirement. Moreover, GBA is very computational efficient, which only requires slightly larger computational cost than GA. [ABSTRACT FROM AUTHOR]