Project Title : Post-operative patient analysis using data mining.
Introduction: The classification task of this database is to determine where patients in a postoperative recovery area should be sent to next. Because hypothermia is a significant concern after surgery, the attributes correspond roughly to body temperature measurements. After surgery body temperature varies .According to various measurements analysis such as
patient internal body temperature, surface temperature it is decided what is the status of patient’s health and whether patient
should be sent to intensive-care unit , general hospital floor or patient is fit enough to go home.
Hardware Requirements :
➢ Intel processor
➢ 1 gb hard disk
➢ 512 mb RAM
➢ Windows xp, vista,windows 7,linux
➢ Turbo c
Methodology : Best-first search is a search algorithm which explores a graph by expanding the most promising node chosen according to a specified rule. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function f(n) which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to that point, and most important, on any extra knowledge about the problem domain. BFS algorithm uses greedy approach to solve the problem. Using a greedy algorithm, expand the first successor of the parent. After a successor is generated
1. If the successor's heuristic is better than its parent, the successor is set at the front of the queue (with the parent reinserted directly behind it), and the loop restarts.
2. Else, the successor is inserted into the queue (in a location determined by its heuristic value). The procedure will evaluate the remaining successors (if any) of the parent.
Attribute information :
1. L-CORE (patient's internal temperature in C):
high (> 37), mid (>= 36 and