Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naive Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

/pdf/accurate-diabetes-risk-stratification-using-machine-learning-364rx4wh10.pdf

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers

Objective Flares in rheumatoid arthritis (RA) and axial spondyloarthritis (SpA) may influence physical activity. The aim of this study was to assess longitudinally the association between patient-reported flares and activity-tracker-provided steps per minute, using machine learning. Methods This prospective observational study (ActConnect) included patients with definite RA or axial SpA. For a 3-month time period, physical activity was assessed continuously by number of steps/minute, using a consumer grade activity tracker, and flares were self-assessed weekly. Machine-learning techniques were applied to the data set. After intrapatient normalization of the physical activity data, multiclass Bayesian methods were used to calculate sensitivities, specificities, and predictive values of the machine-generated models of physical activity in order to predict patient-reported flares. Results Overall, 155 patients (1,339 weekly flare assessments and 224,952 hours of physical activity assessments) were analyzed. The mean ± SD age for patients with RA (n = 82) was 48.9 ± 12.6 years and was 41.2 ± 10.3 years for those with axial SpA (n = 73). The mean ± SD disease duration was 10.5 ± 8.8 years for patients with RA and 10.8 ± 9.1 years for those with axial SpA. Fourteen patients with RA (17.1%) and 41 patients with axial SpA (56.2%) were male. Disease was well-controlled (Disease Activity Score in 28 joints mean ± SD 2.2 ± 1.2; Bath Ankylosing Spondylitis Disease Activity Index score mean ± SD 3.1 ± 2.0), but flares were frequent (22.7% of all weekly assessments). The model generated by machine learning performed well against patient-reported flares (mean sensitivity 96% [95% confidence interval (95% CI) 94-97%], mean specificity 97% [95% CI 96-97%], mean positive predictive value 91% [95% CI 88-96%], and negative predictive value 99% [95% CI 98-100%]). Sensitivity analyses were confirmatory. Conclusion Although these pilot findings will have to be confirmed, the correct detection of flares by machine-learning processing of activity tracker data provides a framework for future studies of remote-control monitoring of disease activity, with great precision and minimal patient burden.

/pdf/detection-of-flares-by-decrease-in-physical-activity-1cf91n2ad5.pdf

Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology.

Type 2 diabetes mellitus is a severe chronic disease threatening human health and has a high incidence worldwide. People need to use effective prediction model to diagnose and prevent diabetes in time. At present, data mining technology has become an increasingly important technology with classification capability in the field of medical diagnosis. This paper proposes a risk prediction model for type 2 diabetes based on ensemble learning method. In the proposed model, the weighted feature selection algorithm based on random forest (RF-WFS) is used for optimal feature selection, and extreme gradient boosting (XGBoost) classifier. The effectiveness of the method was validated by comparing the various performance metrics and the results of different contrast experiments. Additionally, we get a better prediction accuracy using the method than using the other classification algorithms (C4.5, Naive Bayes, AdaBoost, Random Forest). The validation results at UCI Pima Indian diabetes dataset shows that the model has better accuracy and classification performance than other research results mentioned in the literature. As a result, it has been proven that the model would be effective for the diagnosis of diabetes at the initial stage.

A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier

Diabetes is a metabolic disorder and currently is one of the most appalling diseases that mankind is facing. In diabetic disease, the body does not properly respond to “insulin”, an important hormone that converts sugar into energy needed for the proper functioning of regular life. The disease comes with severe complications on our body as it increases the risk of developing kidney disease, heart disease, eye retinal disease, nerve damage, and blood vessel damage. As per the World Health Organization, about 8.8% of the world was diabetic in the year 2017 and they have projected it to reach nearly 10% by 2045. This study develops a model for diabetic prediction based on data mining classifications techniques. Classification of imbalanced data especially in medical informatics is challenging and was the motivational factor for developing a classifier using a rebalancing algorithm. A two-phase classification model is employed in which the first step is preprocessing the data by use of Synthetic Minority Oversampling Technique (SMOTE), and the second one is feeding five classifiers (Bagging, SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), Simple Logistic and Decision Tree) with the preprocessed data in order to select the best classifier for balanced dataset to predict diabetes. We have achieved an accuracy of 94.7013%, and 0.953 receiver operator characteristics (ROC) curve with decision tree classifier. The validation was achieved via a 10-fold cross validation with an experiment that was conducted on clinical records of 734 patients.

Effective Prediction of Type II Diabetes Mellitus Using Data Mining Classifiers and SMOTE

The noteworthy advances in biotechnology and biomedical sciences have prompted a huge creation of information, for example, high throughput genetic information and clinical data, produced from extensive Electronic Health Records. To this end, utilization of machine learning and data mining techniques in biosciences is by and by crucial and fundamental in endeavors to change cleverly all accessible data into profitable knowledge. Diabetes mellitus is characterized as a gathering of metabolic issue applying critical weight on human health around the world. Broad research in all parts of diabetes (determination, pathophysiology, treatment, and so forth.) has prompted the age of tremendous measures of information. The point of the present examination is to direct an orderly audit of the uses of machine-learning, data mining strategies and instruments in the field of diabetes. The main theme of this work is to provide a system which can prognosticate the diabetes in patients with better accuracy. Here, eleven well-known machine-learning algorithms like Naive Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting etc. are used for detection of diabetes at an early stage. The evaluations of all the eleven algorithms are examined on various parameters like accuracy, precision, F-measure and recall. After cross-validation and hyper-tuning, the best three machine-learning algorithms are determined and then used in Ensemble Voting Classifier. The experimental results affirm that the pointed framework can accomplish to outstanding outcome of almost 86% accuracy of the Pima Indians Diabetes Database.

/pdf/a-robust-voting-approach-for-diabetes-prediction-using-17ru4k9gch.pdf

A robust voting approach for diabetes prediction using traditional machine learning techniques

In Data mining classification is one the most important technique. Today, we have data in abundant from numerous sources but in order to get meaningful information from it is very tedious task. Machine learning algorithms to train classifiers to decode the meaningful information from the data, this analysis approach has gained much popularity in recent years. This paper explores evaluation performance of Naive Bayes, Logistic Regression and Decision tree, Random forest using datasets (Pima Indian Diabetes data from UCI Repository). Naive Bayes algorithm is depending upon likelihood and probability; it is fast and stable to data changes. Logistic Regression, calculate the relationship of each feature and weights them based on their impact on result. Random forest algorithm is an ensemble algorithm, fits multiple trees with subset of data and averages tree result to improve performance and control over-fitting. Decision tree can be nicely visualized uses binary tree structure with each node making a decision depending upon the value of the feature. This paper concludes with a comparative evaluation of Naive Bayes, Logistic Regression, Decision tree and Random Forest in the context of Pima Indian Diabetes Dataset( take from UCI repository) in order to predict the diabetic patients.


Keywords: Naive Bayes, Logistic Regression, Random Forest, Classification, Decision tree

https://www.ijarcs.info/index.php/Ijarcs/article/download/3035/3018

Performance Analysis of Classification Algorithms in Predicting Diabetes

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Meraj Nabi

Author Tools

Chat about Author

Papers

Performance Analysis of Classification Algorithms in Predicting Diabetes