To Develop Healthcare Approach using Clustering

Health care domain have attracted considerable amount of research fields. One of the field that has a drastic focus on health care domain is data mining. Mainly health care system focuses on some data mining theories like classification, clustering etc. The backbone in the domain of data mining is the data itself. For any field that is related to data mining, the data should be reliable and huge. System is working on patient’s medical data i.e. electronic health record. A large amount of diagnosed patient’s medical test data is stored electronically on a local machine. The aim is to provide such an unwavering service to the patient so that the patient should have complete knowledge of their disease before going for diagnosis. A system can predict the disease by considering few parameters of the patient’s test. Patient’s disease can be easily detected without wasting days for waiting for their test’s results. This prediction system is implemented by using classification algorithm i.e. semi-supervised heterogeneous graph-based algorithm. The Proposed system should be compatible to provide not only the prediction but should also calculate their prescription, dosage, and health check-up status. Proposed system does not only benefit the patients but the doctors. k-means algorithm is implemented for

[1]  Nigel H. Lovell,et al.  Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data , 2015, Artif. Intell. Medicine.

[2]  Hsiao-Yun Hu,et al.  Cognitive impairment assessed at annual geriatric health examinations predicts mortality among the elderly. , 2014, Preventive medicine.

[3]  Tapio Salakoski,et al.  Predicting patient acuity from electronic patient records , 2014, J. Biomed. Informatics.

[4]  Tommy W. S. Chow,et al.  Compact Graph based Semi-Supervised Learning for Medical Diagnosis in Alzheimer’s Disease , 2014, IEEE Signal Processing Letters.

[5]  Jeffrey Xu Yu,et al.  Learning Phenotype Structure Using Sequence Model , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Mohamed F. Ghalwash,et al.  Extraction of Interpretable Multivariate Patterns for Early Diagnostics , 2013, 2013 IEEE 13th International Conference on Data Mining.

[7]  Hyunjung Shin,et al.  Research and applications: Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data , 2013, J. Am. Medical Informatics Assoc..

[8]  Chee Keong Kwoh,et al.  Positive-unlabeled learning for disease gene identification , 2012, Bioinform..

[9]  Jiamin Liu,et al.  Gene expression data classification based on improved semi-supervised local Fisher discriminant analysis , 2012, Expert Syst. Appl..

[10]  Yizhou Sun,et al.  Ranking-based clustering of heterogeneous information networks with star network schema , 2009, KDD.