Improved Health Record Mining using Supervised Machine Learning with Recommendation Devipriya K.1, Priya V.2, Dhineshkumar K.3 1Scholar, PG and Research Department of Computer Science, Nehru Memorial College (Autonomous), Puthanampatti, India 2Assistant Professor PG and Research Department of Computer Science, Nehru Memorial College (Autonomous), Puthanampatti, India 3Assistnt Professor, Agni College of Technology, Chennai, India Online published on 31 October, 2017. Abstract In medical dataset retrieving of particular health record and classification of patient at risk in earlier stages by data mining technique is widely utilized. Finding out hidden patterns from unlabeled data is a difficult and crucial process. In this thesis a classification algorithm is implemented to classify the records of patient from a huge database and by CADS exact retrieval is attained. Insertion and query are the two methods available in CADS. Patient details are added through insertion and by means of query extraction of specific data is done. It accurately extracts features. Based on this, an attempt to implement an efficient health record classification and utilized it to improve the performance of health classification.. CADs algorithm is used to extract details of a patient and identifies whether the patient is in critical, normal or medium condition. If the patient is going to suffer from a disease it will be attained as result from this algorithm. In addition to this our approach suggests the food to a particular user is done by collaborative filtering is taken further in order to safeguard themselves from upcoming disease problems. Top Keywords Data Mining Techniques, Supervised Machine Learning, CADS algorithm and Risk Factor. Top |