An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus

Abstract

Recently, the use of artificial intelligence based data mining techniques for massive medical data classification and diagnosis has gained its popularity, whereas the effectiveness and efficiency by feature selection is worthy to further investigate. In this paper, we presents a novel method for feature selection with the use of opposite sign test (OST) as a local search for the electromagnetism-like mechanism (EM) algorithm, denoted as improved electromagnetism-like mechanism (IEM) algorithm. Nearest neighbor algorithm is served as a classifier for the wrapper method. The proposed IEM algorithm is compared with nine popular feature selection and classification methods. Forty-six datasets from the UCI repository and eight gene expression microarray datasets are collected for comprehensive evaluation. Non-parametric statistical tests are conducted to justify the performance of the methods in terms of classification accuracy and Kappa index. The results confirm that the proposed IEM method is superior to the common state-of-art methods. Furthermore, we apply IEM to predict the occurrence of Type 2 diabetes mellitus (DM) after a gestational DM. Our research helps identify the risk factors for this disease; accordingly accurate diagnosis and prognosis can be achieved to reduce the morbidity and mortality rate caused by DM.

Machine Learning and Data Mining Methods in Diabetes Research

“…The first category deals with biomarker discovery, which is a task mainly performed through feature selection techniques [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. Following a feature selection step, a classification algorithm is employed to assess the prediction accuracy of the selected features.…”

An Electromagnetism-like Algorithm to Solve Three-dimensional Highway Alignment Problem

“…Examples include medical data classification and prediction of diabetes mellitus [60], sensor distribution strategy to increase the coverage area of wireless Sensor…”

“…It is also interesting to note that Electromagnetism-like algorithm has been found effective to solve system design problems in hybrid forms with other heuristic algorithm [62,64]; and metaheuristic algorithms such as Particle Swarm Optimization, PSO, [71], Simulated Annealing, SA, [64,67]; as well as with conventional optimization methods [65] and in improved formats of EA algorithm [60,68].…”

“…In the recent studies of the following authors, Electromagnetism-like algorithm has been found to be (in their words) superior to the common state-of-the-art methods [60], preferable to (Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) [61], and significantly better than Genetic Algorithm (GA) [62]. EA is reported, also, to outperform Simulated Annealing (SA) and many well-recognized constructive heuristics as well as to yield more effective solutions than PSO [64].…”

Diagnosis of Diabetes by Applying Data Mining Classification Techniques

“…Early detecting of diabetes ultimately reduces cost on health care providers for treating diabetic patients [3][4][5][6][7][8], but it is a challenging task. For early detecting of diabetes, researchers can take advantage of the patient's health care data to convert raw data into meaningful information and extract hidden knowledge by applying data mining such as decision tree or SOM to construct an intelligent predictive model.…”