Diabetes is a major health problem in both developing and developed countries and its incidence is rising dramatically. In this study, we investigate a novel automatic approach to diagnose Diabetes disease based on Feature Weighted Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The proposed model consists of three stages: Firstly, PCA is applied to select an optimal subset of features out of set of all the features. Secondly, Mutual Information is employed to construct the FWSVM by weighting different features based on their degree of importance. Finally, since parameter selection plays a vital role in classification accuracy of SVMs, MCS is applied to select the best parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracy on UCI dataset. The experimental results demonstrate that our method outperforms the previous methods by not only giving more accurate results but also significantly speeding up the classification procedure.
DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis
Australian National University（澳大利亚国立大学）；Commonwealth Scientific and Industrial Research Organisation（英联邦科学与工业研究组织）；Curtin University（科廷大学）；Obuda University（欧布达大学）
Institute for Basic Science (IBS)（基础科学研究院）；Seoul National University（首尔大学）；Pohang University of Science and Technology（浦项科技大学）
Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of…
National University of Sciences & Technology（国家科学与技术大学）；Manchester Metropolitan University（曼彻斯特城市大学）
With advanced data analytical techniques, efforts for more accurate decision support systems for disease prediction are on rise. Surveys by World Health Organization (WHO) indicate a great increase in number of diabetic patients and related deaths each year. Early diagnosis of diabetes is a major concern among researchers and practitioners. The paper presents an application of \textit{Automatic Multilayer Perceptron }which\textit{ }is combined with an outlier detection method \textit{Enhanced Class Outlier Detection using distance based algorithm }to create a prediction framework named as Enha…
Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. Methods: This paper presents the first complete method for automatically explaining results for any machine learnin…
Huma Therapeutics Limited（休玛治疗有限公司）；University of Belgrade（贝尔格莱德大学）；University of Oxford（牛津大学）；University of Exeter（埃克塞特大学）
Diabetes affects over 400 million people and is among the leading causes of morbidity worldwide. Identification of high-risk individuals can support early diagnosis and prevention of disease development through lifestyle changes. However, the majority of existing risk scores require information about blood-based factors which are not obtainable outside of the clinic. Here, we aimed to develop an accessible solution that could be deployed digitally and at scale. We developed a predictive 10-year type 2 diabetes risk score using 301 features derived from 472,830 participants in the UK Biobank da…
Institute of Technology, Nirma University（尼尔玛大学理工学院）
Machine Learning and Artificial Intelligence can be widely used to diagnose chronic diseases so that necessary precautionary treatment can be done in critical time. Diabetes Mellitus which is one of the major diseases can be easily diagnosed by several Machine Learning algorithms. Early stage diagnosis is crucial to prevent dangerous consequences. In this paper we have made a comparative analysis of several machine learning algorithms viz. Random Forest, Decision Tree, Artificial Neural Networks, K Nearest Neighbor, Support Vector Machine, and XGBoost along with feature attribution using SHAP …
Charles Sturt University（查尔斯特大学）；Al Iraqia University（伊拉克大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College（亚太国际学院）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；University of New South Wales（新南威尔士大学）
Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today's scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predicting the onset of Type 2 Diabetes. This research aims to increase the accuracy and Area Under the Curve (AUC) metric while improving the processing time for predicting the onset of Type 2 Diabetes. The proposed system consists of a deep le…
SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening
Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；NeuTigers, Inc.（NeuTigers公司）；AP-HP Avicennes, Bobigny, France（法国AP-HP阿维尼翁医院）；Clinic International Constantine, Constantine, Algeria（阿尔及利亚康斯坦丁国际诊所）
UNIÃO DE ENSINO DO SUDOESTE DO PARANÁ – UNISEP（巴拉那州西南部教育联盟（UNISEP））；FACULDADE EDUCACIONAL DE FRANCISCO BELTRÃO – FEFB（弗朗西斯科贝尔特朗教育学院（FEFB））
Many people are affected by diabetes around the world. This disease may have type 1 and 2. Diabetes brings with it several complications including diabetic retinopathy, which is a disease that if not treated correctly can lead to irreversible damage in the patient's vision. The earlier it is detected, the better the chances that the patient will not lose vision. Methods of automating manual procedures are currently in evidence and the diagnostic process for retinopathy is manual with the physician analyzing the patient's retina on the monitor. The practice of image recognition can aid this det…
Diabetic retinopathy (DR) is a diabetes complication that affects eyes. DR is a primary cause of blindness in working-age people and it is estimated that 3 to 4 million people with diabetes are blinded by DR every year worldwide. Early diagnosis have been considered an effective way to mitigate such problem. The ultimate goal of our research is to develop novel machine learning techniques to analyze the DR images generated by the fundus camera for automatically DR diagnosis. In this paper, we focus on identifying small lesions on DR fundus images. The results from our analysis, which include t…
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校（UCLA））
Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary data collection costs, patient discomfort in medical procedures, and privacy impacts of data collection that require careful consideration in any real-world health analytics system. An efficient solution would only acquire a subset of features based on the value it provides while considering acquisition costs. Moreover, datasets that provide feature costs are very limited, especially in healthcare. In this paper, we pro…
A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran
Fasa University（法萨大学）；Deakin University（迪肯大学）；Queensland University of Technology（昆士兰科技大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；University of Tabriz（大不里士大学）；Iran University of Medical Sciences（伊朗医科大学）；University of British Columbia（不列颠哥伦比亚大学）
National Institute of Technology Karnataka（卡纳塔克邦国家技术研究所）
Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. Manual diagnosis is time consuming and error-prone. In this paper, we propose a novel deep learning based method for automatic screening of retinal fundus images to detect and classify DR based on the severity. The method uses a dual-path configuration of deep neural networks to achieve the objective. In the first step, a modified UNet++ based retinal vessel segmentation is used to create a fundus image that emphasis…
Department of Computer Science University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）；Department of Ophthalmology Central South University（中南大学湘雅医学院（注：原文仅显示Department of Ophthalmology，此处“Central South University”为大学，按要求提取到大学级别））
Diabetic retinopathy (DR) is a primary cause of blindness in working-age people worldwide. About 3 to 4 million people with diabetes become blind because of DR every year. Diagnosis of DR through color fundus images is a common approach to mitigate such problem. However, DR diagnosis is a difficult and time consuming task, which requires experienced clinicians to identify the presence and significance of many small features on high resolution images. Convolutional Neural Network (CNN) has proved to be a promising approach for automatic biomedical image analysis recently. In this work, we inves…
Objectives To develop and evaluate machine learning models to detect suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for diagnostic screening and clinical management. Methods In this retrospective observational noninterventional study using administrative medical claims data from 1,463,089 patients, gradient-boosted decision trees were trained to detect likely NASH patients from an at-risk patient population with a history of obesity, type 2 diabetes mellitus (T2DM), metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-r…
