Designing a Model to Detect Diabetes using Machine Learning (original) (raw)
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Machine Learning Based Diabetes Prediction System: A Novel Approach
International Journal of Innovative Research in Engineering and Management (IJIREM), 2023
The healthcare sector is poised to experience a remarkable transformation with the integration of artificial intelligence. In the realm of healthcare analysis and prediction, the utilization of data science and machine learning applications proves advantageous. Healthcare is emerging as a progressive and promising field for the implementation of data science applications, particularly in Medical Images Analysis, Drug Discovery, Genetics Research, and Predictive Medicine. Diabetes is broadly classified into three main types: type 1, type 2, and gestational diabetes. The primary objective of this research is to develop a Machine Learning Model for the diagnosis of diabetes. Identifying the accurate symptoms in users or individuals with diabetes is a significant challenge for application and the execution of rules. These combinations of knowledge determine whether an individual is a diabetes patient, including its subtypes such as type_1, type_2, and gestational diabetes. The Machine Learning Model underwent testing on a cohort of 150 patients, producing results comparable to those of medical professionals.
How can machine learning be used to predict diabetes
Diabetes is a chronic condition that affects millions of people worldwide and is associated with numerous complications such as cardiovascular disease, blindness, and kidney failure. According to the World Health Organization (WHO), diabetes has already affected 422 million people worldwide. Early detection is key in diabetes because early treatment can prevent serious complications. This paper discusses the use of machine learning in predicting diabetes diagnosis in an individual. We use public dataset from the UCI machine learning repository which uses 520 instances collected from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh. We analyze the dataset using several machine learning models: Naive Bayes Algorithm, Decision Trees, Logistic Regression, Neural Networks, Random Forest, Stochastic Gradient, and Support Vector Machines. The results are then evaluated using 10-fold cross validation. Finally, we propose the best machine learning algorithm to use for diabetes diagnosis given specified input parameters, and we discuss the possibility of the deployment of a diabetes diagnosis tool.
PREDICTION AND DETECTION OF DIABETES USING MACHINE LEARNING
It is apparent from the truth that the occurrence of diabetes mellitus is high and the complication in the prevention of diabetes also increases. Thus, there are many patients who need the required knowledge and skills to enrich their health. In such cases, the patients are needed to visit the diagnostic center for their treatment. Because of this, they lost their time and expenses. In this paper, we are using the Machine Learning algorithms to predict the level of diabetes with future risk and to determine the medications. This idea projected in the paper is to determine the best prediction algorithm with higher accuracy and combine the entire algorithm using voting classifier.
Utilising Machine Learning Algorithms for Predictive Analysis of Diabetes
Diabetes is a category of metabolic illnesses characterised by persistently elevated blood sugar levels. Frequent urination, increased thirst, and increased appetite are all signs of elevated blood sugar. Diabetes can lead to a wide range of repercussions if ignored. Hyperosmolar hyperglycemia, diabetic ketoacidosis, and even mortality are examples of acute complications. With 537 million sufferers worldwide, diabetes is both the deadliest and the most prevalent noncommunicable illness. Diabetes may be brought on by several causes, including being overweight, having high cholesterol, having a family history, being physically inactive, having improper eating habits, etc. One of this disease's most prevalent symptoms is increased urination-those who have diabetes. Big data analytics are very important in the healthcare industry. The size of databases in the healthcare industry is enormous. By employing big data analytics to analyze massive datasets, one may find hidden patterns and information, learn from the data, and make precise predictions. The present strategy has poor classification and prediction accuracy. In this work, we proposed a diabetes prediction model that incorporates a few extrinsic factors that contribute to the development of diabetes together with other widely used indicators like blood sugar, pregnancies, body mass index (BMI), age, skin thickness, outcome diabetes pedigree function etc. The new dataset enhances classification accuracy over the previous one. To improve classification accuracy, a pipeline model for diabetes prediction has also been implemented. Among all the algorithms that have been used such as Logistic Regression, Ada boost, Decision tree classifier, K Neighbor classifier and Random Forest Classifier, the best accuracy is in Random Forest. Estimates of variable significance, or neural networks, are presented via random forests. They also provide a better way to deal with missing data. The variable that appears the most frequently in a given node fills in the gaps left by missing values. Random forests offer the best accuracy of all the categorisation techniques that are currently available. The random forest method can also handle large amounts of data with hundreds of variables. When a class in the data is less frequent than other classes, it can automatically balance data sets. The approach is appropriate for challenging assignments since it handles variables quickly. To further discuss previous research, the paradigm used in this study will be used for ensemble and hybridization Machine Learning. In the future, a more thorough comparison analysis between different datasets and their features may be carried out to pinpoint all the essential characteristics for predicting diabetes. The proposed work has achieved a more thorough comparative analysis between different datasets and their features that may be carried o u t t o pinpoint all the essential characteristics for predicting diabetes. The best and most accurate diabetes prediction algorithm may be found by comparing a wide range of algorithms and algorithm combinations.
2021
Advanced machine-learning techniques are often used for reasoning-based diagnosis and advanced prediction system within the healthcare industry. The methods and algorithms are based on the historical clinical data and factbased Medicare evaluation. Diabetes is a global problem. Each year people are developing diabetes and due to diabetes, a lot of people are going for organ amputation. According to the World Health Organization (WHO), there is a sharp rise in number of people developing diabetes. In 1980, it was estimated that 180 million people with diabetes worldwide. This number has risen from 108 million to 422 million in 2014. WHO also reported that 1.6 million deaths in 2016 due to diabetes. Diabetes occurs due to insufficient production of insulin from pancreas. Several research show that unhealthy diet, smoking, less exercise, Body Mass Index (BMI) are the primary cause of diabetes. This paper shows the use of machine learning that can identify a patient of being diabetic or...
Analysis of Diabetes disease using Machine Learning Techniques: A Review
Journal of Information Technology Management (JITM), 2023
Diabetes is a type of metabolic disorder with a high level of blood glucose. Due to the high blood sugar, the risk of heart-related diseases like heart attack and stroke got increased. The number of diabetic patients worldwide has increased significantly, and it is considered to be a major life-threatening disease worldwide. The diabetic disease cannot be cured but it can be controlled and managed by timely detection. Artificial Intelligence (AI) with Machine Learning (ML) empowers automatic early diabetes detection which is found to be much better than a manual method of diagnosis. At present, there are many research papers available on diabetes detection using ML techniques. This article aims to outline most of the literature related to ML techniques applied for diabetes prediction and summarize the related challenges. It also talks about the conclusions of the existing model and the benefits of the AI model. After a thorough screening method, 74 articles from the Scopus and Web of Science databases are selected for this study. This review article presents a clear outlook of diabetes detection which helps the researchers work in the area of automated diabetes prediction.
A Machine Learning Approach for Prediction of Diabetes Mellitus
International Journal of Emerging Trends in Engineering Research , 2023
Diabetes Mellitus is among chronic diseases and lots of people are suffering with this disease. It may cause many complications and have a high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. There is no doubt that this alarming figure needs great attention. With the rapid development of Machine Learning, machine learning has been applied to many aspects of medical health. There are several Machine learning algorithms that are used to perform predictive analysis in various fields. Predictive analysis in healthcare is a challenging task but ultimately can help practitioners make data informed about a patient's health and treatment. In this project, for experiment purposes, we have taken a dataset which is originally from the National Institute of diabetes and digestive and kidney diseases. All patients here are females at least 21 years old of Pima Indian heritage. By studying the dataset, we must find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. The objective of this project is to diagnostically predict whether the patient has diabetes or not, based on certain diagnostic measurements included in the dataset. We have proposed a diabetes prediction model for better classification of diabetes by applying some popular machine learning algorithms namely, Logistic Regression, Random Forest Algorithm and KNN Algorithm to predict Diabetes.
Prediction of Diabetes Mellitus Using Machine Learning Algorithm
Diabetes Mellitus collectively known for Type 1 ,Type 2 and Gestational Diabetes is a condition that impairs the ability to provide blood sugar in the body. From the statistics of International Diabetes Federation in 2020, 463 Million people in between ages 20-79 which represents the 9.3% of whole world's population are diabetic .This statistics sums up the need of lifestyle changes and seriousness that one should have towards each self. Machine learning is the current trend in predicting and diagnosing diseases. It is vital for predictive analysis by using data and machine learning algorithms to recognize the future outcomes based on the data available or historical data. Our paper has extracted available diabetic data from Institution and Hospital and has provided a solution to predict diabetes providing a powerful insight. The models used in Machine Learning to predict diabetes are the Linear Regression, Support Vector Machine. Other algorithms require more computational time and Deep Learning algorithms requires a larger dataset .Hence in this paper, we have considered using classical algorithms.
Diabetes Mellitus Prediction using Machine Learning Algorithms
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Diabetes mellitus is related to the high sugar level in the blood. According to the International Diabetes Federation (IDF), there are currently 422 million diabetic people worldwide or 7.7% of the world's population, and this number is expected to rise to 350 billion by 2030. Furthermore, 3.8 million deaths are attributable to diabetes complications every year with, an annual increase of 2.7% from 1990. In this paper, we have proposed the system to predict diabetes using a machine learning algorithm. Early detection of diabetes mellitus would lead to a decrease in the mortality rate. This paper presents an algorithm for naïve Bayes and KNN, which we have implemented using C#. KNN gave the highest accuracy (100%) compared to other algorithms. The other algorithms used are naïve Bayes, Decision tree, Logistic Regression, Random Forest, Support vector machine. A dataset that we have used to build this product contains 21 columns. This product helps in decreasing the mortality rate.
Diabetes Prediction Using Machine Learning
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
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.