This document discusses using data mining techniques to holistically forecast the onset of diabetes. It proposes augmenting an existing diabetes dataset with additional attributes related to common diabetes symptoms. The augmented dataset is analyzed using association rule mining algorithms like Apriori and FP-Growth to generate rules for predicting diabetes status. Several high confidence rules are presented that integrate multiple attributes to determine whether a patient is likely to be diabetic or not diabetic based on their symptoms. The study suggests this holistic approach using an expanded dataset could more accurately predict diabetes onset compared to prior studies using only conventional datasets.
Diagnosis of Diabetes Mellitus Using Machine Learning TechniquesIRJET Journal
The document discusses diagnosing diabetes mellitus using machine learning techniques. It analyzes medical records of diabetics using two classification algorithms: Random Forest and Support Vector Machine (SVM). The SVM algorithm is analyzed using different kernel functions and the best one is selected for prediction. Random Forest uses decision trees to make predictions. The purpose is to predict diabetes and compare the accuracy of the two algorithms to find the best for diabetes prediction.
Prediction of Heart Disease in Diabetic patients using Naive Bayes Classifica...Editor IJCATR
The objective of our paper is to predict the risk of heart disease in diabetic patients. In this research paper we are applying Naive Bayes data mining classification technique which is a probabilistic classifier based on Bayes theorem with strong (naive) independence assumptions between the features. Data mining techniques have been widely used in health care systems for prediction of various diseases with accuracy. Health care industry contains large amount of data and hidden information. Effective decisions are made with this hidden information by applying data mining techniques. These techniques are used to discover hidden patterns and relationships from the datasets. The major challenge facing the healthcare industry is the provision for quality services at affordable costs. A quality service implies diagnosing patients correctly and treating them effectively. In this proposed system certain attributes are consider in diabetic patients to predict the risk of heart disease
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
The document discusses predicting the onset of diabetes among women aged 21 and older using binary classification models. It analyzes medical data from 691 training and 77 test patients using eight features to train two models - a boosted decision tree and decision jungle. Both models achieved over 76% accuracy in predicting diabetes onset. The document concludes the models can help identify at-risk patients for timely treatment intervention and help healthcare systems prevent early deaths and health risks through targeted prevention strategies.
Diagnosis of diabetes using classification mining techniques [IJDKP
Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing
Decision Tree and Naïve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.
This document provides guidelines for managing type 2 diabetes in older adults aged 70 and over. It was created by an international group of diabetes experts to address the special issues related to providing high-quality diabetes care for older populations. The guidelines aim to individualize care based on a person's functional status and medical comorbidities. It provides recommendations in areas like cardiovascular risk, education, renal impairment, and end-of-life care. The guidelines also emphasize the importance of comprehensive geriatric assessments and involving informal caregivers in diabetes management for older adults.
Our aim is to alleviate human suffering related to diabetes and its complications among those least able to withstand the burden of the disease. From 2002 to March 2017, the World Diabetes Foundation provided USD 130 million in funding to 511 projects in 115 countries. For every dollar spent, the Foundation raises approximately 2 dollars in cash or as in-kind donations from other sources. The total value of the WDF project portfolio reached USD 377 million, excluding WDF’s own advocacy and strategic platforms.
Diagnosis of Diabetes Mellitus Using Machine Learning TechniquesIRJET Journal
The document discusses diagnosing diabetes mellitus using machine learning techniques. It analyzes medical records of diabetics using two classification algorithms: Random Forest and Support Vector Machine (SVM). The SVM algorithm is analyzed using different kernel functions and the best one is selected for prediction. Random Forest uses decision trees to make predictions. The purpose is to predict diabetes and compare the accuracy of the two algorithms to find the best for diabetes prediction.
Prediction of Heart Disease in Diabetic patients using Naive Bayes Classifica...Editor IJCATR
The objective of our paper is to predict the risk of heart disease in diabetic patients. In this research paper we are applying Naive Bayes data mining classification technique which is a probabilistic classifier based on Bayes theorem with strong (naive) independence assumptions between the features. Data mining techniques have been widely used in health care systems for prediction of various diseases with accuracy. Health care industry contains large amount of data and hidden information. Effective decisions are made with this hidden information by applying data mining techniques. These techniques are used to discover hidden patterns and relationships from the datasets. The major challenge facing the healthcare industry is the provision for quality services at affordable costs. A quality service implies diagnosing patients correctly and treating them effectively. In this proposed system certain attributes are consider in diabetic patients to predict the risk of heart disease
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
The document discusses predicting the onset of diabetes among women aged 21 and older using binary classification models. It analyzes medical data from 691 training and 77 test patients using eight features to train two models - a boosted decision tree and decision jungle. Both models achieved over 76% accuracy in predicting diabetes onset. The document concludes the models can help identify at-risk patients for timely treatment intervention and help healthcare systems prevent early deaths and health risks through targeted prevention strategies.
Diagnosis of diabetes using classification mining techniques [IJDKP
Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing
Decision Tree and Naïve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.
This document provides guidelines for managing type 2 diabetes in older adults aged 70 and over. It was created by an international group of diabetes experts to address the special issues related to providing high-quality diabetes care for older populations. The guidelines aim to individualize care based on a person's functional status and medical comorbidities. It provides recommendations in areas like cardiovascular risk, education, renal impairment, and end-of-life care. The guidelines also emphasize the importance of comprehensive geriatric assessments and involving informal caregivers in diabetes management for older adults.
Our aim is to alleviate human suffering related to diabetes and its complications among those least able to withstand the burden of the disease. From 2002 to March 2017, the World Diabetes Foundation provided USD 130 million in funding to 511 projects in 115 countries. For every dollar spent, the Foundation raises approximately 2 dollars in cash or as in-kind donations from other sources. The total value of the WDF project portfolio reached USD 377 million, excluding WDF’s own advocacy and strategic platforms.
A correlation study to determine the effect of diabetes self management on di...Kurt Naugles M.D., M.P.H.
Self-Management in this presentation refers to those activities people undertake in an effort to promote health, prevent disease, limit illness, and restore well being. Several investigators contend that self-management be made a major component of many patient health-care strategy (Glasgow, et al., 2001; Wagner, et al., 2001). Currently, nearly 125 million Americans suffer from chronic debilitating illnesses (Anderson, 2000). These national figures clearly underscore the need to develop a multidimensional approach in regards to disease management. Accordingly, measures that incorporate the patient’s perspective in managing his or her health should be explored.
Diabetes mellitus is among those conditions suspected to be highly influenced by self-management activities (Sprangers, et. al., 2000). If benefits do indeed exist, they need to be fully evidenced. The investigation presented here sought to examine the role self management plays in the health outcomes of individuals living with diabetes.
Preliminary Diagnostic System for Endocrine related diseasesandreigumabao
This document provides background information on developing a preliminary diagnostic system for endocrine diseases. It discusses diabetes and thyroid disorders, which can cause nail discoloration, brittleness, and other changes. Previous research has found glycated keratin in fingernails can indicate tissue damage in diabetic patients. The objectives of this study are to design a prototype diagnostic system that captures fingernail images and temperature, analyzes for possible diabetes or thyroidism, and prints/emails results. The scope is limited to these two diseases and depends on severity levels for accuracy.
Approach to Support Diabetes through Data Visualization DivyaBastola
Used Tableau to created a Geo-map by zip codes, Bar chart by sex and race, and another Bar chart by age to display the dense of diabetes prevalence in 17 zip codes of North Texas.
Literature Review is conducted to demonstrate the reduction of hyperglycemia events after the implementation of an inpatient multidisciplinary glucose control management program.
Created Info-graphic to exhibit the ways to manage diabetes through education, counseling, meal/diet, and exercise and potential comorbidities in the diabetic patient that undergoes surgeries.
IRJET- Diabetes Prediction by Machine Learning over Big Data from Healthc...IRJET Journal
This document discusses using machine learning techniques to predict diabetes based on healthcare data. It proposes using preprocessing, K-means clustering, and support vector machine (SVM) classification. Preprocessing cleans and structures the data. K-means clusters the data into groups. SVM classification then predicts whether patients are diabetic or non-diabetic, aiming for a prediction accuracy of 94.9%. The techniques aim to allow for early diabetes prediction using a combination of machine learning methods on both structured and unstructured healthcare data.
Memorias Conferencia Científica Anual sobre Síndrome Metabólico 2017 - Programa Científico
Futuro en el tratamiento de la DM2
Dr. Guillermo E. Umpierrez
Professor of Medicine in the Division of Endocrinology at Emory University School of Medicine, Section Head, Diabetes and Endocrinology. USA. Editor en Jefe del BJM Open Diabetes Research and Care
A Systematic Review Of Type-2 Diabetes By Hadoop Map-ReduceFinni Rice
This document summarizes a systematic review of using Hadoop/MapReduce algorithms to analyze big data related to type 2 diabetes. It first provides background on the growing issue of big data in healthcare. It then reviews literature discussing how big data analytics and cloud computing can improve healthcare services and outcomes by more effectively collecting, managing, and using healthcare information. The review focuses on analyzing different parameters of type 2 diabetes using big data to better understand demographic and geographic variations in the disease and identify other factors impacting healthcare outcomes.
This document provides standards of care for diabetes management and treatment. It summarizes guidelines for classifying and diagnosing diabetes, testing for pre-diabetes and diabetes in asymptomatic individuals, and detecting and diagnosing gestational diabetes. The standards are intended to improve diabetes outcomes by providing evidence-based recommendations to help clinicians provide quality care and treatment.
Evolving Role of Digital Biomarkers in HealthcareCitiusTech
As the adoption of remote monitoring, wearable devices and mobile applications grows, digital biomarkers will play a significant role in better disease identification and health management.
Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm...IRJET Journal
This document discusses several machine learning algorithms that have been used to predict diabetes, including KNN, Naive Bayes, Random Forest, J48, SVM, logistic regression, decision trees, neural networks, and ensemble models. It analyzes past research applying these methods to diabetes prediction and reports their accuracy results. The document then proposes using an ensemble hybrid model combining KNN, Naive Bayes, Random Forest, and J48 algorithms to predict diabetes with increased performance and accuracy compared to individual techniques.
This document presents a system for remotely monitoring the health of diabetic patients. The system has two main components: 1) an intelligent device that monitors patients' blood glucose, blood pressure, and other readings and sends the data to the patient's phone via Bluetooth, and 2) a cloud-based module where the data is stored and accessible to doctors. The cloud-based module includes educational resources for patients and decision support tools for doctors. The system allows for 24/7 monitoring of patients' health and ensures timely updates and education are provided to help manage their condition.
Diabetic is a well known public health problem of today. There are many risk factors of it, which can be identified in pre-diabetic state. So the present study was conducted with the aim to know the status of anthropometric and haematological parameters in pre-diabetic states. For this hospital based study pre-diabetic subjects were identified from first degree relatives of type 2 DM Patients, enrolled in diabetic research centre P.B.M. hospital Bikaner. Relevant investigations were done. Data thus collected on semi-structured questionnaire and analysed using content analysis. Data analysis revealed that although mean Body Mass Index (BMI) was within normal range but Waist circumference (WC), West Hip (W/H) Ratio, Systolic blood pressure were higher than the normal range accepted for that parameter. But mean value of all the studied haematological parameter were within the normal range accepted for that parameter. So it can be conclude that anthropology of an individual may be associated with the pre-diabetic state. Hypertension was found in 25.35% of pre-diabetics. Further researches are necessary to find out this possible association of anthropologic parameter and pre-diabetic state.
Non-invasive Diagnostic Tools: Cardiometabolic Risk Assessment and Predictionasclepiuspdfs
Cardiometabolic risks (CMRs) have rapidly increased to epidemic proportions worldwide in the past three decades. Cardiovascular disease (CVD) remains the number one killer. No country has reduced, reversed, or prevented the increase in the incidence or prevalence of chronic metabolic diseases. Framingham Heart Study group described the modifiable risk factors that promote the development of CVD. They also developed risk calculators, for the prediction of acute vascular events such as heart attacks and stroke. The risk predictor algorithms were fine-tuned, as and when additional risk factors were discovered. However, at the time of this writing, there is no such calculator for assessment, stratification, and management of CMRs. On the other hand, numbers of non-invasive diagnostic devices have been developed for continuous monitoring of blood pressure and glucose profiles. We have described in our earlier articles, non-invasive diagnostic platform developed by LD-Technologies,
Early Stage Diabetic Disease Prediction and Risk Minimization using Machine L...IRJET Journal
This document reviews machine learning techniques for early prediction and risk minimization of diabetic disease. It discusses how various machine learning algorithms like decision trees, KNN, random forests, and SVM have been applied to diabetes prediction datasets. Accuracy rates of 83.11% to 88.42% were achieved for different algorithms. Feature selection techniques like Pearson correlation were also able to improve some algorithm accuracies further. The document proposes using machine learning systems to better diagnose and care for diabetic patients early on.
In developing countries like India, the plan of subsidising basic domestic commodities for poor families is an important part of meeting people’s basic needs. When a self-contained system for ration distribution is available, it benefits cardholders in a variety of ways. The E-Ration is the most convenient way to purchase ration items. Its goal is to offer ration products on the internet. E-Ration Shop is an automated system that distributes the exact amount of ration to cardholders based on the type of ration card and the number of family members, as well as keeping track of transactions in a database. As a result, this approach will reduce dealer communication, manual calculations, and time spent in stores. The cardholder can access the rationing system at any time during the month, with no need for human participation in the ration shop. Each transaction is automatically logged into the database by this system. Customers who purchase ration items online will have their information saved in an internet database, which will be viewable to higher-ranking officers as well as the shop owner. A fair price store (FPS) or ration shop is another name for a public distribution shop. It is a part of the Government of India's public distribution system, which provides subsidised rations to the needy in India. The Civil Supplies Corporation is a key government agency that oversees and distributes basic supplies to all inhabitants. Various products like as rice, sugar, and kerosene are supplied utilising a traditional ration store method in that system. Some of the drawbacks of the traditional ration shop system include: The user is unable to obtain an accurate quantity of material due to laborious measurements in the traditional technique. Because it is a direct process done by online ration shop keeper, this application uploads the data immediately to the server, confirming the data. Because it is a direct process done by online ration shop keeper, they cannot do anything in these transactions as they do in paper work.
PSEDM-DOH WorkshopDiabetes Management Training Using Insulin v_7 - 20170321.pptxRhoda Isip
1) A fasting blood glucose level of 126 mg/dL or higher on two separate tests.
2) A two-hour plasma glucose level of 200 mg/dL or higher during a 75g oral glucose tolerance test.
3) A random plasma glucose of 200 mg/dL or higher for someone with classic symptoms of hyperglycemia.
This document provides an executive summary of key findings from the IDF Diabetes Atlas Seventh Edition:
- An estimated 415 million adults worldwide have diabetes in 2015, including 193 million who are undiagnosed. An additional 318 million adults have impaired glucose tolerance putting them at high risk.
- By 2040, without effective prevention, the number of people with diabetes is projected to rise to 642 million.
- In 2015, diabetes caused an estimated 5.0 million deaths and cost between $673-1197 billion in healthcare expenditures.
- The largest numbers of people with diabetes live in the South East Asia and Western Pacific regions, though Africa is projected to have the largest growth between 2015-2040.
The document discusses diabetes risk assessment that can be performed in dental offices. It provides background on the presenters and objectives of assessing diabetes risk. Key points covered include the importance of recognizing diabetes symptoms, common risk factors, and diagnostic tests for diabetes and pre-diabetes that can help identify undiagnosed cases. A dual testing method combining fasting glucose and HbA1c is proposed to improve diabetes detection rates.
Talk about data visualization as tool to add new value to health data, presented in the Panel: Old School Data Set, Rebooted, Repurposed and Creating Killer New Value Health Datapalooza, June 2, 2015
Diabetes prediction using machine learningdataalcott
This document discusses a proposed system to classify and predict diabetes using machine learning and deep learning algorithms. The objectives are to classify the PIMA Indian diabetes dataset and design an interactive application where users can input data to get a prediction. The proposed system uses support vector machine (SVM) for machine learning and neural networks for deep learning. It aims to improve accuracy over existing systems by using deep learning techniques. The methodology involves collecting a dataset, preprocessing, splitting for training and testing, applying algorithms, and evaluating results.
A Survey of Ontology-based Information Extraction for Social Media Content An...ijcnes
The amount of information generated in the Web has grown enormously over the years. This information is significant to individuals, businesses and organizations. If analyzed, understood and utilized, it will provide a valuable insight to its stakeholders. However, many of these information are semi-structured or unstructured which makes it difficult to draw in-depth understanding of the implications behind those information. This is where Ontology-based Information Extraction (OBIE) and social media content analysis come into play. OBIE has now become a popular way to extract information coming from machine-readable sources. This paper presents a survey of OBIE, Ontology languages and tools and the process to build an ontology model and framework. The author made a comparison of two ontology building frameworks and identified which framework is complete.
Economic Growth of Information Technology (It) Industry on the Indian Economyijcnes
Information Technology (IT) is an important emerging sector of the Indian Economy. IT in India is an industry comprising of two noteworthy segments IT administrations and business process outsourcing (BPO).The segment has expanded its commitment to Indias GDP from 1.2% in 1998 to 9.3% in 2015. According to NASSCOM, the segment amassed incomes of US$147 billion out of 2015, with send out income remaining at US$99 billion and household income at US$48 billion, developing by more than 13%.Indias present Prime Minister Narendra Modi has begun a venture called �DIGITAL INDIA i.e., Computerized India to help secure IT a position both inside and outside of India. The IT sector has served as a fertile ground for the growth of a new entrepreneurial class with innovative corporate practices and has been instrumental in reversing the brain drain, raising Indias brand equity and attracting foreign direct investment (FDI) leading to other associated benefits. The Size of this sector has increased at a tremendous rate of 35% per year during the last 10 years. This Paper examines the India�s growth in IT industry and also studied the impact of IT on the Indian Economy.
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Self-Management in this presentation refers to those activities people undertake in an effort to promote health, prevent disease, limit illness, and restore well being. Several investigators contend that self-management be made a major component of many patient health-care strategy (Glasgow, et al., 2001; Wagner, et al., 2001). Currently, nearly 125 million Americans suffer from chronic debilitating illnesses (Anderson, 2000). These national figures clearly underscore the need to develop a multidimensional approach in regards to disease management. Accordingly, measures that incorporate the patient’s perspective in managing his or her health should be explored.
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This document provides background information on developing a preliminary diagnostic system for endocrine diseases. It discusses diabetes and thyroid disorders, which can cause nail discoloration, brittleness, and other changes. Previous research has found glycated keratin in fingernails can indicate tissue damage in diabetic patients. The objectives of this study are to design a prototype diagnostic system that captures fingernail images and temperature, analyzes for possible diabetes or thyroidism, and prints/emails results. The scope is limited to these two diseases and depends on severity levels for accuracy.
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Literature Review is conducted to demonstrate the reduction of hyperglycemia events after the implementation of an inpatient multidisciplinary glucose control management program.
Created Info-graphic to exhibit the ways to manage diabetes through education, counseling, meal/diet, and exercise and potential comorbidities in the diabetic patient that undergoes surgeries.
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This document discusses using machine learning techniques to predict diabetes based on healthcare data. It proposes using preprocessing, K-means clustering, and support vector machine (SVM) classification. Preprocessing cleans and structures the data. K-means clusters the data into groups. SVM classification then predicts whether patients are diabetic or non-diabetic, aiming for a prediction accuracy of 94.9%. The techniques aim to allow for early diabetes prediction using a combination of machine learning methods on both structured and unstructured healthcare data.
Memorias Conferencia Científica Anual sobre Síndrome Metabólico 2017 - Programa Científico
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This document summarizes a systematic review of using Hadoop/MapReduce algorithms to analyze big data related to type 2 diabetes. It first provides background on the growing issue of big data in healthcare. It then reviews literature discussing how big data analytics and cloud computing can improve healthcare services and outcomes by more effectively collecting, managing, and using healthcare information. The review focuses on analyzing different parameters of type 2 diabetes using big data to better understand demographic and geographic variations in the disease and identify other factors impacting healthcare outcomes.
This document provides standards of care for diabetes management and treatment. It summarizes guidelines for classifying and diagnosing diabetes, testing for pre-diabetes and diabetes in asymptomatic individuals, and detecting and diagnosing gestational diabetes. The standards are intended to improve diabetes outcomes by providing evidence-based recommendations to help clinicians provide quality care and treatment.
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This document discusses several machine learning algorithms that have been used to predict diabetes, including KNN, Naive Bayes, Random Forest, J48, SVM, logistic regression, decision trees, neural networks, and ensemble models. It analyzes past research applying these methods to diabetes prediction and reports their accuracy results. The document then proposes using an ensemble hybrid model combining KNN, Naive Bayes, Random Forest, and J48 algorithms to predict diabetes with increased performance and accuracy compared to individual techniques.
This document presents a system for remotely monitoring the health of diabetic patients. The system has two main components: 1) an intelligent device that monitors patients' blood glucose, blood pressure, and other readings and sends the data to the patient's phone via Bluetooth, and 2) a cloud-based module where the data is stored and accessible to doctors. The cloud-based module includes educational resources for patients and decision support tools for doctors. The system allows for 24/7 monitoring of patients' health and ensures timely updates and education are provided to help manage their condition.
Diabetic is a well known public health problem of today. There are many risk factors of it, which can be identified in pre-diabetic state. So the present study was conducted with the aim to know the status of anthropometric and haematological parameters in pre-diabetic states. For this hospital based study pre-diabetic subjects were identified from first degree relatives of type 2 DM Patients, enrolled in diabetic research centre P.B.M. hospital Bikaner. Relevant investigations were done. Data thus collected on semi-structured questionnaire and analysed using content analysis. Data analysis revealed that although mean Body Mass Index (BMI) was within normal range but Waist circumference (WC), West Hip (W/H) Ratio, Systolic blood pressure were higher than the normal range accepted for that parameter. But mean value of all the studied haematological parameter were within the normal range accepted for that parameter. So it can be conclude that anthropology of an individual may be associated with the pre-diabetic state. Hypertension was found in 25.35% of pre-diabetics. Further researches are necessary to find out this possible association of anthropologic parameter and pre-diabetic state.
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Cardiometabolic risks (CMRs) have rapidly increased to epidemic proportions worldwide in the past three decades. Cardiovascular disease (CVD) remains the number one killer. No country has reduced, reversed, or prevented the increase in the incidence or prevalence of chronic metabolic diseases. Framingham Heart Study group described the modifiable risk factors that promote the development of CVD. They also developed risk calculators, for the prediction of acute vascular events such as heart attacks and stroke. The risk predictor algorithms were fine-tuned, as and when additional risk factors were discovered. However, at the time of this writing, there is no such calculator for assessment, stratification, and management of CMRs. On the other hand, numbers of non-invasive diagnostic devices have been developed for continuous monitoring of blood pressure and glucose profiles. We have described in our earlier articles, non-invasive diagnostic platform developed by LD-Technologies,
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This document reviews machine learning techniques for early prediction and risk minimization of diabetic disease. It discusses how various machine learning algorithms like decision trees, KNN, random forests, and SVM have been applied to diabetes prediction datasets. Accuracy rates of 83.11% to 88.42% were achieved for different algorithms. Feature selection techniques like Pearson correlation were also able to improve some algorithm accuracies further. The document proposes using machine learning systems to better diagnose and care for diabetic patients early on.
In developing countries like India, the plan of subsidising basic domestic commodities for poor families is an important part of meeting people’s basic needs. When a self-contained system for ration distribution is available, it benefits cardholders in a variety of ways. The E-Ration is the most convenient way to purchase ration items. Its goal is to offer ration products on the internet. E-Ration Shop is an automated system that distributes the exact amount of ration to cardholders based on the type of ration card and the number of family members, as well as keeping track of transactions in a database. As a result, this approach will reduce dealer communication, manual calculations, and time spent in stores. The cardholder can access the rationing system at any time during the month, with no need for human participation in the ration shop. Each transaction is automatically logged into the database by this system. Customers who purchase ration items online will have their information saved in an internet database, which will be viewable to higher-ranking officers as well as the shop owner. A fair price store (FPS) or ration shop is another name for a public distribution shop. It is a part of the Government of India's public distribution system, which provides subsidised rations to the needy in India. The Civil Supplies Corporation is a key government agency that oversees and distributes basic supplies to all inhabitants. Various products like as rice, sugar, and kerosene are supplied utilising a traditional ration store method in that system. Some of the drawbacks of the traditional ration shop system include: The user is unable to obtain an accurate quantity of material due to laborious measurements in the traditional technique. Because it is a direct process done by online ration shop keeper, this application uploads the data immediately to the server, confirming the data. Because it is a direct process done by online ration shop keeper, they cannot do anything in these transactions as they do in paper work.
PSEDM-DOH WorkshopDiabetes Management Training Using Insulin v_7 - 20170321.pptxRhoda Isip
1) A fasting blood glucose level of 126 mg/dL or higher on two separate tests.
2) A two-hour plasma glucose level of 200 mg/dL or higher during a 75g oral glucose tolerance test.
3) A random plasma glucose of 200 mg/dL or higher for someone with classic symptoms of hyperglycemia.
This document provides an executive summary of key findings from the IDF Diabetes Atlas Seventh Edition:
- An estimated 415 million adults worldwide have diabetes in 2015, including 193 million who are undiagnosed. An additional 318 million adults have impaired glucose tolerance putting them at high risk.
- By 2040, without effective prevention, the number of people with diabetes is projected to rise to 642 million.
- In 2015, diabetes caused an estimated 5.0 million deaths and cost between $673-1197 billion in healthcare expenditures.
- The largest numbers of people with diabetes live in the South East Asia and Western Pacific regions, though Africa is projected to have the largest growth between 2015-2040.
The document discusses diabetes risk assessment that can be performed in dental offices. It provides background on the presenters and objectives of assessing diabetes risk. Key points covered include the importance of recognizing diabetes symptoms, common risk factors, and diagnostic tests for diabetes and pre-diabetes that can help identify undiagnosed cases. A dual testing method combining fasting glucose and HbA1c is proposed to improve diabetes detection rates.
Talk about data visualization as tool to add new value to health data, presented in the Panel: Old School Data Set, Rebooted, Repurposed and Creating Killer New Value Health Datapalooza, June 2, 2015
Diabetes prediction using machine learningdataalcott
This document discusses a proposed system to classify and predict diabetes using machine learning and deep learning algorithms. The objectives are to classify the PIMA Indian diabetes dataset and design an interactive application where users can input data to get a prediction. The proposed system uses support vector machine (SVM) for machine learning and neural networks for deep learning. It aims to improve accuracy over existing systems by using deep learning techniques. The methodology involves collecting a dataset, preprocessing, splitting for training and testing, applying algorithms, and evaluating results.
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Holistic Forecasting of Onset of Diabetes through Data Mining Techniques
1. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.108-111
ISSN: 2278-2400
108
Holistic Forecasting of Onset of Diabetes through
Data Mining Techniques
S.Sankaranarayanan1
, T.Pramananda Perumal2
Research Scholar, R & D Centre, Bharathiar University, Coimbatore, India &
1
Associate professor of Computer Science, Government Arts College, Kumbakonam, India
2
Principal, Presidency College, Chennai, India
Email:profsankaranarayanan@yahoo.in1
, pramanandaperumal@yahoo.com2
Abstract— Diabetes is one of the modern day diseases that poses
serious threat for the affected and is ever challenging for
physicians who are involved in its management and
control.Type2 diabetes mellitus ranges in exponential rating day
by day in its increase. Mere not being aware of the facts and
causes that can lead to such state, unawareness about diabetic
symptoms and late detection make diabetic condition
unmanageable and is really a challenging task to be faced all
victims. This paper suggests holistic measures and means by
which any common man can get into it to check whether he / she
is a would-be victim of Diabetes through simple checking of
symptoms that may lead to Diabetic condition, analyses the
factual causes of the aforesaid disease. This would certainly
make a person to ensure for the locus-centric state of whether of
being a diabetic or not. The problem of diagnosing the onset and
incidence of Diabetes is addressed more with a data mining
approach in mind. As the success of any data mining approach is
solely dependant on the underlying dataset upon which learning
is manifested and taken for, this paper inspects more on locating
prima-facie symptoms of diabetes disorder. A sagacious insight
of analyzing the actual causes of diabetes is set and hence a
comprehensive set of data for diabetic condition is proposed
here. Subjecting this data to data analysis through simple data
mining techniques v.i.z., FP-Growth and Apriori would certainly
model a holistic inference engine that could help a doctor to be
more astute in confirming the diabetic condition of patients.
Association rules are also being inducted based on both of these
approaches. A heuristic computer aided diagnosis (CAD) system
for diabetes can be built upon this.
Keywords— Holistic forecasting of Diabetes, Association rule
mining (ARM) for Diabetes, Augmented dataset for Diabetes,
Computer Aided Diagnosis (CAD) for Diabetes
I. INTRODUCTION
Diabetes mellitus, a metabolic disorder is a state of the body by
which it is not properly coping with the synthesis of glucose
level to blood stream and other organs of the body normally due
to lack of Insulin hormone. Type1, the Insulin Dependant
Diabetes Mellitus is the worst case compared to type2 in which
no Insulin is required for the management of this disorder and
sustainability. Dietary modifications, bodily exercise are more
useful and sometimes anti-diabetic drugs are believed to offer
remedy in such cases. A complex analysis by a trained and
competent doctor marks the first phase in the treatment process.
While the basic causes of diabetes are genetic
susceptibility or insulin resistance, other contributing factors are
as being given here below
Sedentary lifestyle
Physical inactivity and lack of exercise
Excessive sleep
Overeating and consequent obesity
Mental stress and strain
The early signs of diabetes should not be ignored at any cost
because once intensified, this disease can lead to tough
complications like heart disease, stroke, foot ulcers, kidney
failure, and eye damage. Normally before the incidence of
diabetic symptoms, nobody cares about this deadly disease.It is
customary on the part of the doctors to investigate the leading
signs and symptoms with a set of pre-determined bio-parameters
and conclude whether a person is really diabetic or not. Only
patents’ attributes such as blood glucose level (BGL), glycated
hemoglobin (HbA1c), age, sex etc. are normally taken for
consideration by field experts for disease analysis. Though an
appreciable amount of efforts have been put by both doctors and
patients, diabetes remains to be an ever challenging for its
management. Also no mechanism is available as of now to
accurately predict whether a non-diabetic or pre-diabetic would
probably develop diabetes in future. The reason is mostly
because of that the latent causes and the corresponding
parameters are often ignored or omitted miserably by medical
experts. And now time has come for us to pour a holistic insight
into this disease and to appropriately inspect the ground reasons
which incidentally cause diabetes culminated. In this paper,
various holistic measures are suggested to fill the gap which
prevails in early prediction of diabetes. The success rate of this
holistic system of prediction using various data mining
techniques would certainly work out to a 100% hit and success.
II. THE BACKGROUND
A. Data mining for diabetic health care
Data mining of databases nowadays has become a dependable
mechanism to predict decisions including diseases like heart
attack, diabetic mellitus and other complicated ailments [1].
Data mining addresses to both knowledge extraction and
machine learning mostly by using Classification, Clustering and
Association techniques. Applying a particular data mining
technique suiting a particular application is domain centric.
Classification consists of predicting a certain outcome or goal
based on a given instance of input values. It uses a training set,
2. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.108-111
ISSN: 2278-2400
109
which maps a set of attributes to the predictor attribute. The
algorithm tries to discover relationships between the attributes
that would make it possible to predict the outcome. Next the
algorithm is given a data set not seen before which contains the
same set of attributes, except for the class label which is not yet
known. Diabetes disease diagnosis via proper interpretation of
the diabetes data is an important classification problem.
Statistical analysis of attribute oriented data and mining
associative logic rules from a larger data are also the classical
approaches to data mining. Decision tree is yet another model in
which a tree node denotes a test on parametric attributes and the
leaves can model classes [2]. Cluster analysis deals with data
objects without a consultative (known) class label and thus
contributes to unsupervised learning. Data mining algorithm is
used for testing the accuracy in predicting diabetic status [3].
B. Data set for Diabetic analysis through Data mining
Diabetic patients are often classified by well-known benchmark
data set available at the University of California- the Irvine data
repository, the so-called “Pima Indians” diabetic database and
dataset (PIDD), which is a collection of medical diagnostic
reports of 768 examples from a native-American population
living near Phoenix, Arizona. This data set was used to develop
algorithms to predict whether a patient shows signs of diabetes
according to 1990 World Health Organization criteria. Thus
WHO has proposed a set of nine attributes as depicted in Table I;
all are physiological parameters and medical test results for
diagnosing diabetes disease.
Table I – PIDD
Item.No Item
1 Pregnancy recurrence / times
2 Plasma glucose level from Oral Glucose
Forbearance Test -OGFT(mg /dL)
3 Diastolic blood tension (mm/Hg)
4 Triceps skin fold thickness (mm)
5 2-hour serum insulin (μU/ml)
6 BMI, the Body Mass Value (kg/m2)
7 Diabetes Pedigree function
8 Age (years)
9 Disease Status (0-Healthy, 1-Diabetes)
C. Holistic observation of Diabetic Symptoms
Diabetic disease condition of patients is often diagnosed by
Expert-doctors with a set of pre-conceived parameters, the
so-called patient symptoms as depicted in Table1. Yet they lag in
to have a holistic insight which is required to be put in place in
lieu of the traditional and existing diabetic analysis pattern by
which diabetes can be predicted comprehensively and
holistically. To understand more of diabetic symptoms
holistically, it is worthwhile to see the factors proposed by the
consultant of Endocrinology and Diabetology, Moolchand
Medcity, New Delhi. Dr Sanjiv Bhambani portrays the top 12
symptoms of diabetes disorder [4] as follows:
Disproportionate increase of Thirst (Polydypsia)
Extreme dryness of Mouth
Excessive Hunger (Polyphagia)
Frequent Urination and associate Urine Infection
(Polyuria)
Sudden Weight Loss
Fatigue
Blurred Vision
Headaches
Change in Skin Complexion ,Dry skin, Itching
Slow healing Wounds
More skin and / or Yeast infections
Numbness of Limbs, Tingling of hands and feet
D. The proposed data set for Holistic mining of Diabetes
By exploring the root causes which are reasonably responsible
for patients to have the incidence of Diabetes, there is need to
append with more of attributes in the existing system which
evaluates and predicts Diabetes disease. This augmentation in
the diabetes dataset would certainly make the diabetic diagnosis
more viable, reliable and exploring. Any computer aided
diagnosis system designed upon this augmentation would be
steadfast and consistent in predicting the incidence of diabetes in
any case as to be either pre-diabetic or would-be diabetic. The
proposed data set is deliberated here in Table II.
Table II – (a) the Augmented Dataset for Diabetic Analysis
Item.No Item
1 Pregnancy recurrence / times
2 Plasma glucose level from Oral Glucose
Forbearance Test -OGFT(mg /dL)
3 Diastolic blood tension (mm/Hg)
4 Triceps skin fold thickness (mm)
5 2-hour serum insulin (μU/ml)
6 BMI, the Body mass value (kg/m2)
7 Diabetes Pedigree function
8 Age (years)
9 Polydypsia & Dried Mouth (1-Yes, 0-No)
10 Polyphagia (1-Yes, 0-No)
11 Polyuria (1-Yes, 0-No)
12 Abrupt Weight Loss (1-Yes, 0-No)
13 Visionary Blurring (1-Yes, 0-No)
Table II – (b) the Augmented Dataset for Diabetic Analysis
14 Sudden change in Skin Contour & Itching &
Infections (1-Yes, 0-No)
15 Numbness of Hands - Limbs and Tingling of
Palms – Soles (1-Yes, 0-No)
16 Excessively Fatigue and Headache Status
(1-Yes, 0-No)
17 Disease Status (0-Healthy, 1-Diabetes)
Any database built on the above attributes would certainly be
information rich in all possible directions that would make it
more dependable in the task of predicting diabetes. It has 17
attributes; all being numeric variables out of which the last
attribute is the target variable which predicts the status of
3. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.108-111
ISSN: 2278-2400
110
diabetes holistically.These attributes are, the PIDD proposed
plus the holistic parameters identified here in this paper. They
are summarized here below:
1. Pregnancy recurrence / times
2. Two Hour plasma glucose - OGFT
3. Diastolic blood tension
4. Triceps skin fold thickness
5. Two hour serum insulin
6. BMI
7. Diabetes pedigree function
8. Age
Apart from the PIDD attributes as given above, other factors
which can also have a determining role in diabetic condition are
carefully identified and appended to be the new attributes here in
this paper. They are merely conditional attributes as given below
as to be either prevalent or not (1- if prevalent, 0-if not prevalent)
9. Polydypsia & Dried Mouth
10. Polyphagia
11. Polyuria
12. Abrupt Weight Loss
13. Visionary Blurring
14. Sudden change in Skin Contour & Itching ,
Infections
15. Numbness of Hands /Limbs and Tingling of Palms/
Soles
16. Excessively Fatigue and Headache Status
All the above 16 values holistically predict the disease status, the
target variable i.e if the subjected patient is diabetic within 3
years (class=1) or not (class=0).
III. LITERATURE SURVEY
Surveying of literature was conducted for the above problem
area and extracted that most of the studies [2], [3], [5], [6] have
applied data mining only to the conventional PIDD and the
results obtained of these studies were not stupendous because of
the fact that these have missed to make mention of all possible
factors that have either active or passive role in causing diabetes,
as mentioned here in the augmented dataset given in Table II.
But data mining study if made on this proposed dataset, a
superset to PIDD would be more viable in precisely predicting
the diabetic condition of human race.
IV. DATA MINING METHODS FOR DIABETES DATA
Data mining process can be extremely useful for medical
practitioners for extracting hidden medical knowledge.
Prediction models are the core data mining methods mainly used
in healthcare and engineering field and the techniques used in
such prediction are shown in Fig. 1. Performance evaluation is
done by comparing various models used and accuracy is
measured thereof.
Fig. 1 Steps in Data mining predicting models
A. Data mining through Association Rule Mining (ARM)
ARM establishes relation among the set of items included in a
database [5] by means of a strategy similar to a market-bucket
analysis. Any co-occurrence of instances of data items of a
database are identified as relationships and decomposed as rules
by ARM. Thus association rule mining can mine probable
causes of diabetes that can be used for better clinical diagnosis
[6]. The generalized framework of this method is summarized
here.
An association rule has the form X⇒Y, where X, Y⊂ I, the data
instance set & X∩Y=Ø. The rule X⇒Y holds in the transaction
set D
X also contain Y. The rule X⇒Y has support s in the transaction
ntains X∪Y [6], [7]. The
rules so generated are usually pruned according to some notion
of confidence. An efficient data storage used here is the T-tree,
the total support tree which uses set enumeration to store
frequent itemsets [6].
B. Apriori and Frequent Pattern growth
Ariori algorithm finds frequent itemsets from the database and
thus identifies association rules. Frequent pattern growth
(FP-Growth) also discovers frequent itemset without candidate
itemset generation. FP-Growth is a two-step process as given
below:
Step 1: A data structure called the Frequent Pattern tree
(FP-tree) is built over the given data set
Step 2: Frequent itemsets are formulated from the FP-tree
V. RESULTS AND DISCUSSION
Association rule mining generates very useful rules for the
diabetes dataset proposed here. Apriori and FP-growth have
generated same set of rules from the augmented dataset. These
rules provide valuable knowledge and have the potential to
improve clinical decision making in diabetes disease. Some of
the rules which comply to 100.0% confidence are summarized
here below:
1. IF (Age >=40) AND (Polyphagia = 1) AND (Weight
Loss = 0) THEN Not Diabetic
2. IF (Age >=65) AND (Polyphagia = 1) AND (Weight
Loss = 0) AND (Numbness/Tingling =1) THEN Not
Diabetic
3. IF (Age >=70) AND (Polyphagia = 1) AND (Weight
Loss = 1) AND (Numbness/Tingling =1) AND
(Polyuria = 0) THEN Not Diabetic