A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus

Abstract

Lots of research has been carried out globally to design a machine classifier which could predict it from some physical and bio-medical parameters. In this work a hybrid machine learning classifier has been proposed to design an artificial predictor to correctly classify diabetic and non-diabetic people. The classifier is an amalgamation of the widely used K-means algorithm and Gravitational search algorithm (GSA). GSA has been used as an optimization tool which will compute the best centroids from the two classes of training data; the positive class (who are diabetic) and negative class (who are non-diabetic). In K-means algorithm instead of using random samples as initial cluster head, the optimized centroids from GSA are used as the cluster centers. The inherent problem associated with k-means algorithm is the initial placement of cluster centers, which may cause convergence delay thereby degrading the overall performance. This problem is tried to overcome by using a combined GSA and K-means.

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

“…Therefore, there is a requirement for detecting it before any decision. For predicting diabetes, the data used are (1) the plasma glucose concentration;(2) diastolic blood pressure;(3) 2-h serum insulin (mu U/mL);(4) body mass index;(5) the diabetes pedigree function;(6) age in years [29][30][31]. These data can be collected using health sensors embedded in smart phones.…”

“…The health parameters chosen to predict the risk of hypertension are (1) systolic blood pressure (SBP); (2) diastolic blood pressure (DBP); (3) total cholesterol (TC); (4) high-density lipoprotein (HDL); (5) low-density lipoprotein (LDL); (6) plasma glucose concentration (PGC) and (7) HR [28]. To detect diabetes mellitus, the data considered are (1) PGC; (2) DBP; (3) 2-h serum insulin (mu U/mL); (4) body mass index (BMI); (5) the diabetes pedigree function; (6) age in years [29][30][31]. These data are collected from the users and stored in the fog nodes for synthesis and analysis.…”

Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy

“…However, parameter tuning has shown improvement up to a certain extent. In a more concise study, the researchers have tried to merge the existing approach with other intelligent techniques 13,14 and compared the overall result based on comparisons. However, hybridizing suffers from the drawbacks of an increased computational cost and never focuses on improving either approach.…”