Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.

It is a common view that the assembly process heavily affects a product's final quality and cost. The continuously shortening product life cycle requires a faster response speed as well as a lower defect rate in assembly production. In this situation, assembly quality control is becoming one of the most demanding problems in the modern manufacturing environment. The main causes of assembly defects can be classified into four categories, i.e., improper design, defective part, variance in assembly system, and operator error. The first three categories have been studied for many decades. However, elements of the operator error have not been fully explored. In this paper, using a copier assembly as an example, the problem of assembly defects caused by mistakes of operators is investigated systematically. A novel defect-rate prediction model is derived from the study of two complexity factors, namely, the design-based assembly complexity factor and the process-based assembly complexity factor, which are defined according to the structure and production characteristics of the copier. Several case studies consistently demonstrate that the new prediction model is accurate and stable for evaluating the copier assembly quality. Moreover, another case study offered in this paper demonstrates that the prediction model can provide effective assistance in the improvement of assembly quality.