A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases

• Published in: Journal of medical systems Vol. 38; no. 5; pp. 48 - 12
• Main Authors: Yilmaz, Nihat, Inan, Onur, Uzer, Mustafa Serter
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.05.2014; Springer Nature B.V
• Subjects: Algorithms; Classification; Cluster Analysis; Clustering; Computational Biology - methods; Data analysis; Data Interpretation, Statistical; Diabetes; Diabetes Mellitus - classification; Diabetes Mellitus - diagnosis; Diagnosis; Diagnosis, Computer-Assisted - methods; Diseases; Health Informatics; Health Sciences; Heart; Heart Diseases - classification; Heart Diseases - diagnosis; Humans; Medical diagnosis; Medicine; Medicine & Public Health; Statistics for Life Sciences; Topical Collection on Transactional Processing Systems; Transactional Processing Systems; Modified K-means Algorithm; Heart and Diabetes diseases; Support Vector Machine
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-014-0048-7

The most important factors that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. This article presents a new data preparation method based on clustering algorithms for diagnosis of heart and diabetes diseases. In this method, a new modified K-means Algorithm is used for clustering based data preparation system for the elimination of noisy and inconsistent data and Support Vector Machines is used for classification. This newly developed approach was tested in the diagnosis of heart diseases and diabetes, which are prevalent within society and figure among the leading causes of death. The data sets used in the diagnosis of these diseases are the Statlog (Heart), the SPECT images and the Pima Indians Diabetes data sets obtained from the UCI database. The proposed system achieved 97.87 %, 98.18 %, 96.71 % classification success rates from these data sets. Classification accuracies for these data sets were obtained through using 10-fold cross-validation method. According to the results, the proposed method of performance is highly successful compared to other results attained, and seems very promising for pattern recognition applications.