Enhancing the Performance of Heart Disease Prediction from Collecting Cleveland Heart Dataset using Bayesian Network

Cardiovascular diseases are diseases affecting the general well-being of the heart. It is responsible for many deaths annually. Consequently, this paper focuses on improving the performance of heart disease prediction by collecting Cleveland heart datasets from the University of California Irvine machine learning repository. Different feature subset selection is performed on the dataset and modeled using machine learning models such as logistic regression, K-Nearest neighbor, Naïve Bayes and Bayesian Network. The proposed method achieved an accuracy of 88.53%. Based on the results obtained, we observed feature reduction on the Cleveland dataset could enhance the performance of the Bayesian network

Practical feature subset selection for machine learning

Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithm

Real-time food intake classification and energy expenditure estimation on a mobile device

© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment