Abstract—Supervised and unsupervised learning techniques are increasingly applied to improve medical decision-making. Medical-recorded data also have accumulated large amount of information about patients and their medical conditions. Relationship and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this paper, we propose projected clustering method for generating clusters of similar bio-signal patterns from medical data to be analyzed and the various classification methods for reflecting information of heart signal on the classification/prediction model. The experiments show that the optimal cluster is constructed by applying PROCLUS algorithm and it has from 0.881 to 0.9 f1-value index of prediction under test data.

Index Terms—Heart disease, ECG, de-identification, projected clustering and medical big data.

Heon Gyu Lee and Hyun-Sup Kang are with GAION Ltd. Cheongrim-Bldg, 19 Samsung-ro 96 Gil, Kangnam-Gu, Seoul, Republic of Korea (email: hglee@gaion.kr).
Jong Seol Lee and Keun Ho Ryu are with Chungbuk National Univ. Chungdae-ro 1, Seowon-Gu, Cheongju, ChungbuK, Republic of Korea.

Cite: Heon Gyu Lee, Jong Seol Lee, Hyun-Sup Kang, Keun Ho Ryu, "Projected Clustering Methods for Predicting Heart Disease," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 9, no. 2, pp. 73-81, 2019.