Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2

Abstract

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status.

Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors

“…The importance of cluster analyses has also been highlighted by a study of Ryu et al, who developed a screening model including gender-specific characteristics for the estimation of an undiagnosed diabetes mellitus in high-risk patients for development of diabetes mellitus, namely patients with a positive family history of diabetes [ 16 ]. Additionally, more recently, Nedyalkova et al showed that k -means clustering to detect clinical variables might be useful to stratify type 2 diabetics into distinct subgroups of risk factors [ 17 ].…”

Hybrid diabetes disease prediction framework based on data imputation and outlier detection techniques

“…Nedyalkova et al (2021) applied the combinatorial k‐means algorithm for prediction the diabetes mellitus type 2. In this work, authors explore all possible aspects of k‐means algorithm with predetermined descriptor and groups.…”

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

“…With the advancement of electronic medical record (EMR) and artificial intelligence, machine learning (ML) approaches have been developed as part of precision medicine to assist in clinical decision-making, including disease detection, medical imaging, and explainable risk prediction [21][22][23][24][25][26][27][28][29]. In recent years, unsupervised ML algorithms have been utilized to reveal the patterns of diseases such as diabetes and cardiovascular diseases [30][31][32][33]. Consensus clustering is an unsupervised ML technique used to identify patterns of data, and provides a visualization tool to inspect cluster numbers, membership, and boundaries [34].…”