An Experimental Approach Toward Type 2 Diabetes Diagnosis Using Cultural Algorithm

Patil, Ratna; Tamane, Sharvari; Patil, Kanishk

doi:10.1007/978-981-15-8289-9_39

Cited by 3 publications

(1 citation statement)

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“…Different feature selection methods to select informative attributes and filter out the irrelevant attributes and evolutionary multi-objective approaches, individual or hybrid ones can be employed to obtain better Pareto-optimal fronts. Patil et al [18] experimented the cultural algorithms with a series of emerging algorithms with the aim of extracting cultural evolution by following the dual inheritance. The conventional genetic algorithm selects the fittest to converge towards the results indeed does not taken into account the diversity of the population.…”

Section: Literature Reviewmentioning

confidence: 99%

A modified mayfly-SVM approach for early detection of type 2 diabetes mellitus

Patil

Tamane²,

Rawandale

et al. 2022

IJECE

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<p>Diabetes mellitus is a chronic disease that affects many people in the world badly. Early diagnosis of this disease is of paramount importance as physicians and patients can work towards prevention and mitigation of future complications. Hence, there is a necessity to develop a system that diagnoses type 2 diabetes mellitus (T2DM) at an early stage. Recently, large number of studies have emerged with prediction models to diagnose T2DM. Most importantly, published literature lacks the availability of multi-class studies. Therefore, the primary objective of the study is development of multi-class predictive model by taking advantage of routinely available clinical data in diagnosing T2DM using machine learning algorithms. In this work, modified mayfly-support vector machine is implemented to notice the prediabetic stage accurately. To assess the effectiveness of proposed model, a comparative study was undertaken and was contrasted with T2DM prediction models developed by other researchers from last five years. Proposed model was validated over data collected from local hospitals and the benchmark PIMA dataset available on UCI repository. The study reveals that modified Mayfly-SVM has a considerable edge over metaheuristic optimization algorithms in local as well as global searching capabilities and has attained maximum test accuracy of 94.5% over PIMA.</p>

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Section: Literature Reviewmentioning

confidence: 99%