Machine learning techniques are widely used in medical decision support systems. Medical diagnosis helps to obtain different features representing the different variations of the disease. With the help of different diagnostic procedures, it is likely to have relevant, irrelevant and redundant features to represent a disease. Redundant features contribute to the wrong classification of the disease. Therefore, removing the redundant features reduces the size of the data and computation complexity. Identifying a good feature subset for effective classification is a non-trivial task. This requires an exhaustive search over the sample space of the dataset. The main objective of this paper is to use a metaheuristic algorithm to determine the optimal feature subset with improved classification accuracy in cardiovascular disease diagnosis. Swarm intelligence based Artificial Bee Colony (ABC) algorithm is used to find the best features in the disease identification. To evaluate the fitness of ABC, Support Vector Machine (SVM) classification is used. The performance of the proposed algorithm is validated against the Cleveland Heart disease dataset taken from the UCI machine learning repository. The experimental results show that, ABC-SVM performs better than Feature selection with reverse ranking. The results also show that, the proposed method obtained good classification accuracy with only seven features.

Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm.

The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.

Privacy preserving data mining is a vibrant area in data mining. The sharing of data between the organizations is found to be beneficial for business growth. However, privacy policies and threats prevent the data owners from sharing the data for mini