Data Farming Algorithm with Temporal Medical Events Mohd

Data farming is a method to generate large amounts of data through simulation of several configurations from large parameter space and then analysed for pattern. More data are helpful for extracting the hidden pattern. In this paper, we propose an algorithm for the data farming in the medical domain. The proposed algorithm merges, data farming with temporality of cardiac patient's medical history. The proposed algorithm is helpful to estimate the correct dose of the dobutamine for the patients with heart disease. ‘Dose’ of the patient is affected by the temporal events occurred with the patient such as (1) diabetic, (2) myocardial infarction (MI) or heart attack, (3) revascularization by percutaneous transluminal coronary angioplasty (PTCA) and (4) coronary artery bypass grafting surgery (CABG). ‘Dose’ is not only affected by these events, but also affected by the time of occurrence of the event. The proposed algorithm uses a weight function to account for time of occurrence of these events and characteristics. Result shows that, - the proposed algorithm is efficient to farm the temporal data of the heart patient with the correct ‘dose’ in the treatment.