The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex "what if" queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platforms.
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Research Article: International Journal of Sensor Networks and Data Communications
Scientific Tracks Abstracts: Journal of Civil and Environmental Engineering
Scientific Tracks Abstracts: Journal of Civil and Environmental Engineering
Scientific Tracks Abstracts: Advances in Robotics & Automation
Scientific Tracks Abstracts: Advances in Robotics & Automation
Posters & Accepted Abstracts: Journal of Material Sciences & Engineering
Posters & Accepted Abstracts: Journal of Material Sciences & Engineering
Posters-Accepted Abstracts: Journal of Material Sciences & Engineering
Posters-Accepted Abstracts: Journal of Material Sciences & Engineering
Scientific Tracks Abstracts: Journal of Material Sciences & Engineering
Scientific Tracks Abstracts: Journal of Material Sciences & Engineering