scholarly journals Diabetes Prediction Using Machine Learning

2021 ◽  
pp. 487-493
Author(s):  
Abhishek Ballewar ◽  
Yashoneel Shukla ◽  
Ronel Yumnam ◽  
Ahamed Shihan ◽  
Umakant Mandawkar
Keyword(s):  
Machine Learning ◽  
Medical Care ◽  
Support Vector ◽  
Diabetic Patients ◽  
Learn Strategy ◽  
Programming Techniques ◽  
Diabetes Prediction ◽  
Human World ◽  
Coronary Failure ◽  
Present Life

Diabetes has developed as one the riskiest danger to the human world. Many are turning into its casualties and can't emerge from it paying little heed to the way that they are attempting to stay away from it for becoming further. Distributed computing and Internet of Things (IoT) are two devices that assume a vital part in the present life with respect to numerous perspectives and purposes including medical care checking of patients and older society. Diabetes Medical care Checking Administrations are vital these days on the grounds that and that too far off medical care checking in light of the fact that actually going to emergency clinics and remaining in a line is exceptionally incapable rendition of patient observing. On the off chance that a patient has exceptionally constant diabetes and he spends his/her time remaining in a line anything perilous can happen to him/her at any occasion of time. Thus, this paper concocted shrewd sensors and distinctive machine learning calculations like xgboost calculation, arbitrary woods. Diabetes can likewise go about as a implies for different infections like coronary failure, kidney harm and fairly visual impairment. This paper can utilize different AI calculations, for example, support vector machine, straight relapse, choice tree, xgboost and arbitrary woods with the assistance of which can without much of a stretch discover the all-out effectiveness and precision of foreseeing that a human will experience the ill effects of diabetes or not. There are differently numerous customary strategies which are entirely unexpected from programming techniques that can analyse diabetes and anticipate pre states of diabetic patients. Diabetics is caused because of a tremendous uphill in the blood divide containing glucose. There is an advancement plot accessible using train test split and K overlap cross approval utilizing Sklit learn strategy.

scholarly journals Diabetes Prediction Using Machine Learning

2020 ◽  
pp. 294-305
Author(s):  
KM Jyoti Rani
Keyword(s):  
Machine Learning ◽  
Blood Glucose ◽  
Data Science ◽  
International Diabetes Federation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Blood Glucose ◽  
Diabetes Prediction ◽  
The One ◽  
Prediction Of Diabetes

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes is a disease caused due to the increase level of blood glucose. This high blood glucose produces the symptoms of frequent urination, increased thirst, and increased hunger. Diabetes is a one of the leading cause of blindness, kidney failure, amputations, heart failure and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our pancreas is supposed to release insulin. Insulin serves as a key to open our cells, to allow the glucose to enter and allow us to use the glucose for energy. But with diabetes, this system does not work. Type 1 and type 2 diabetes are the most common forms of the disease, but there are also other kinds, such as gestational diabetes, which occurs during pregnancy, as well as other forms. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. The algorithms like K nearest neighbour, Logistic Regression, Random forest, Support vector machine and Decision tree are used. The accuracy of the model using each of the algorithms is calculated. Then the one with a good accuracy is taken as the model for predicting the diabetes.

eDiaPredict: An Ensemble-based Framework for Diabetes Prediction

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-26
Author(s):  
Ashima Singh ◽  
Arwinder Dhillon ◽  
Neeraj Kumar ◽  
M. Shamim Hossain ◽  
Ghulam Muhammad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Critical Conditions ◽  
Support Vector ◽  
Diabetic Patients ◽  
Medical Systems ◽  
Diabetes Status

Medical systems incorporate modern computational intelligence in healthcare. Machine learning techniques are applied to predict the onset and reoccurrence of the disease, identify biomarkers for survivability analysis depending upon certain health conditions of the patient. Early prediction of diseases like diabetes is essential as the number of diabetic patients of all age groups is increasing rapidly. To identify underlying reasons for the onset of diabetes in its early stage has become a challenging task for medical practitioners. Continuously increasing diabetic patient data has necessitated for the applications of efficient machine learning algorithms, which learns from the trends of the underlying data and recognizes the critical conditions in patients. In this article, an ensemble-based framework named e DiaPredict is proposed. It uses ensemble modeling, which includes an ensemble of different machine learning algorithms comprising XGBoost, Random Forest, Support Vector Machine, Neural Network, and Decision tree to predict diabetes status among patients. The performance of eDiaPredict has been evaluated using various performance parameters like accuracy, sensitivity, specificity, Gini Index, precision, area under curve, area under convex hull, minimum error rate, and minimum weighted coefficient. The effectiveness of the proposed approach is shown by its application on the PIMA Indian diabetes dataset wherein an accuracy of 95% is achieved.

scholarly journals Implementation of machine learning algorithms to create diabetic patient re-admission profiles

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Mohamed Alloghani ◽  
Ahmed Aljaaf ◽  
Abir Hussain ◽  
Thar Baker ◽  
Jamila Mustafina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Diabetic Patients ◽  
K Nearest Neighbor ◽  
Critical Approach ◽  
Linear Discriminant ◽  
Applied Machine Learning ◽  
Learning Machine

Abstract Background Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. Methods In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k–Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. Results Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. Conclusion Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

scholarly journals Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning

2019 ◽  
Vol 26 (1) ◽  
pp. 703-718 ◽  
Author(s):  
Josep Vehí ◽  
Iván Contreras ◽  
Silvia Oviedo ◽  
Lyvia Biagi ◽  
Arthur Bertachi
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Diabetic Patients ◽  
Glucose Levels ◽  
Prediction And Prevention

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.

scholarly journals Machine Learning Methods for Diabetes Prediction

2019 ◽  
Vol 8 (12) ◽  
pp. 2199-2205
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Research Field ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Health Practitioners ◽  
Prediction Technique ◽  
Diabetes Prediction ◽  
Machine Learning Models

Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor

scholarly journals Type II Diabetes Prediction using Combo of SVM

2019 ◽  
Vol 8 (6) ◽  
pp. 712-715
Keyword(s):  
Machine Learning ◽  
Type Ii Diabetes ◽  
Human Life ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Techniques ◽  
Healthcare It ◽  
Diabetes Prediction ◽  
Almost All ◽  
Two Sides

In 21th century, IT plays a very important and helpful role in health care industries acting as a savior to human life. Data mining and machine learning are two sides of healthcare-IT. Proposed system considers one of the most common chronic diseases called diabetes. India and almost all other countries are worried about diabetic patients, so diabetes can termed as a global chronic disease. In this paper, well-known predictive machine learning techniques viz. SVM, Random Tree and ANN are applied on PIMA dataset. Results of SVM, ANN, and RT are 90.1%, 88.02%, and 83.59% respectively

scholarly journals IDMPF: intelligent diabetes mellitus prediction framework using machine learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Leila Ismail ◽  
Huned Materwala
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Health Professionals ◽  
Prediction Models ◽  
Critical Examination ◽  
Support Vector ◽  
Learning Models ◽  
Content Type ◽  
Diabetes Prediction ◽  
Training Methodologies

PurposeMachine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.Design/methodology/approachHealth professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.FindingsThe authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.Originality/valueThis paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

scholarly journals A Machine Learning Technique for Reducing Hospital Readmissions for Diabetic Diseases

2019 ◽  
Vol 9 (2) ◽  
pp. 4878-4884
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Operating Characteristic ◽  
Hospital Readmissions ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Diabetic Patients ◽  
Machine Learning Technique ◽  
Hospital Records ◽  
Learning Technique

The number of readmissions in diabetic diseases keeps increasing from time to time in patients from various hospitals. This brings a dreadful name to the hospital and is also considered as an act of irresponsibility of the doctors. So in order to reduce the readmissions of diabetic patients, we propose an approach which uses a machine learning technique to compare the hospital records of various patients. We have used various diabetic dataset features for our technique to predict the readmission probability rates of patients. We compared our proposed technique with existing Machine Learning algorithms like Random Forest, K-means clustering, Support Vector Machine(SVM) and found the best possible prediction with proposed approach using receiver operating characteristic( ROC) curve.

scholarly journals A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Raja Krishnamoorthi ◽  
Shubham Joshi ◽  
Hatim Z. Almarzouki ◽  
Piyush Kumar Shukla ◽  
Ali Rizwan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Support Vector ◽  
Model Assessment ◽  
Learning Models ◽  
Research Issues ◽  
Diabetes Prediction

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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