K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services

Abstract

Nowadays, eHealth service has become a booming area, which refers to computer-based health care and information delivery to improve health service locally, regionally and worldwide. An effective disease risk prediction model by analyzing electronic health data benefits not only to care a patient but also to provide services through the corresponding data-driven eHealth systems. In this paper, we particularly focus on predicting and analysing diabetes mellitus, an increasingly prevalent chronic disease that refers to a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. K-Nearest Neighbor (KNN) is one of the most popular and simplest machine learning techniques to build such a disease risk prediction model utilizing relevant health data. In order to achieve our goal, we present an optimal K-Nearest Neighbor (Opt-KNN) learning based prediction model based on patient's habitual attributes in various dimensions. This approach determines the optimal number of neighbors with low error rate for providing better prediction outcome in the resultant model. The effectiveness of this machine learning eHealth model is examined by conducting experiments on the real-world diabetes mellitus data collected from medical hospitals.

A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction

“…In line with Rajendra and Latifi [50], Joshi et al [51] revealed that the LR-based diabetes prediction model could predict type 2 diabetes for US-PIMA Indian diabetes dataset by up to 78.26%. Diabetes prediction using K-NN also showed good performance applied in US-PIMA Indian diabetes dataset which is conducted by Premamayudu et al [54] and Sarker et al [55]. The accuracy achieved 79% and the ROC achieved around 73%.…”

“…This method has been widely used for the classification and prediction of diabetes [40]. Numerous previous studies have developed and analyzed diabetes prediction models utilizing machine learning methods such as multilayer perceptron (MLP) [46][47][48], logistic regression (LR) [16,[49][50][51][52], K-neural networks (K-NN) [52][53][54], decision tree (DT) [55][56][57][58], Naïve Bayes (NB) [59][60][61][62][63], random forest (RF) [64][65][66][67][68][69], and extreme gradient boosting (XGB) [69][70][71][72]. Mohapatra et al [47], Butt et al [48], and Bani-Salameh et al [52] utilized a machine learning-based approach for the classification, early-stage identification, and prediction of diabetes.…”

Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis

“…This method is used to measure the performance of classification algorithms with an accuracy rate of a percent (%). Classification performance can be evaluated using Equation 7 to obtain accuracy, error rate, precision, recall, and specificity values [22]- [24].…”

Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods

“…Iqbal Sarker et al [18] developed a model for providing eHealth services for diabetes patients. The optimal k-nearest neighbor technique is used for diabetes mellitus prediction and analysis.…”