Predictive Data Mining for Disease Diagnosis: an Overview for Poultry Disease Prediction
Huge amount of data is produced in medical organizations but this data is not properly used. There is a treasure of hidden facts present in the datasets. The
healthcare environment is still “facts rich” but “awareness poor”. There is a lack of effective analysis tools to determine hidden relationships and trends in data. Advanced data mining techniques can help cure this situation. For this determination we can use different data mining techniques. This research paper aims to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Poultry disease. This research has developed a prototype Poultry disease using data mining techniques specifically, Decision Trees, Naïve Bayes and Neural Network. This Poultry disease prediction system can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age (days), symptoms, weather and surroundings it can predict the probability of Poultry infected by disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to Poultry disease, to be established.