ArticleJournal of medical systems2014
A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases.
Article in Journal of medical systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- The Application of Unsupervised Clustering Methods to Alzheimer's Disease.Frontiers in computational neuroscience · 2019Pooled it
- Improved chaos-enhanced FOX for clustering-based supervised medical classification.Scientific reports · 2026Article
- Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.Brain sciences · 2021Article
- Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data.Sensors (Basel, Switzerland) · 2020Article
- A Systematic Mapping Study of Data Preparation in Heart Disease Knowledge Discovery.Journal of medical systems · 2018Article
- A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases.Journal of medical systems · 2015Article
- The application of data mining techniques to oral cancer prognosis.Journal of medical systems · 2015Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.