Diabetes Prediction Using Machine Learning Algorithms (original) (raw)

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.

Survey Paper on Diabetes Risk Prediction using Machine Learning Algorithm

International Journal of Scientific Research in Science, Engineering and Technology, 2022

Diabetes Mellitus (DM) is a chronic, lifelong metabolism disorder. It affects the ability of the body system to use the energy found in food. The improper management of the disease will lead to Heart disease, kidney disease, eye disease, nerve disease and pregnancy complications. Classification model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. Big Data Analytics plays an significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. In existing method, the classification and prediction accuracy is not so high. In this paper, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc. Classification accuracy is boosted with new dataset compared to existing dataset. Further with imposed a pipeline model for diabetes prediction intended towards improving the accuracy of classification.

Prediction of Diabetes Using Machine Learning Algorithm

SSRN Electronic Journal, 2019

Diabetes mellitus has become a pandemic in both developed and developing countries. It is estimated that by 2030 diabetes affected people will be around 100 million in India. Diabetes is most common type of disease found in the people of age from 41 to 60 due to inheritance, unhealthy diet causing obesity, reduced Insulin resistance and negative effects caused due to urbanization. Limited knowledge about diabetes causes a various adverse effect in health and it is necessary to spread awareness about Diabetes. To address this problem a Diabetes prediction portal has been developed which is used to get a dichotomous outcome. PIMA India diabetes dataset is used, and machine learning is used to train the data and k nearest neighbours provided highest accuracy was thus used in deployment. Flask web framework was used to handle HTTP requests of the predictions. HTML page was created to display the predictions.

Survey on Predictive Analysis of Diabetes Disease Using Machine Learning Algorithms

IJCSMC, 2020

Big data analysis is predicated on large amount of data. Diabetes is caused due to the excessive amount of sugar condensed into the blood. One of the most critical chronic healthcare problems is diabetes.Undiagonosed diabetes problem may leads to damage eyes, heart, kidneys and nerves of diabetes patients. If improper medication taken is done which also lead to death. Early detection of diabetes is very important to maintain healthy life. Machine learning algorithm to identify a best predicting algorithm based various matrices such us accuracy, precision, recall, F-measure, sensitivity and specificity. This paper discusses about various ML techniques to predict the Diabetes disease by using dataset .Machine learning algorithm namely Decision Tree, SVM, Naive Bayes, Random forest, k-NN, K-mean clustering and LR algorithms are used in their experiment to detect diabetes at an early stage. Experiments are performed on Pima Indian diabetes dataset (PIDD) which is sourced from UCI Machin...

Prediction of Diabetes Disease Using Machine Learning Model

Lecture Notes in Electrical Engineering

As per the statistics mentioned by the world health organization, four hundred twenty-two million people in the world are suffering from diabetes which has raised the death toll to 1.6 million per year. This unprecedented growth in the number of cases and the number of casualties has led to an alarming situation because the data statistics represent a significant increase in diabetic cases among the young population, 18 years of age. Diabetes leads to various health hazards such as dysfunction of the kidney, cardiovascular problems, lower limb dismembering, and retinopathy. This article builds up a model for the prediction of diabetes using machine learning. The supervised machine learning algorithms used for prediction model such as decision tree, Naïve Bayes, artificial neural network, and logistic regression. Further, the comparison of these methods has been done based on various performance parameters such as accuracy, recall, precision, and F-score. Keywords Machine Learning(ML) • Artificial neural network (ANN) • Logistic regression • Decision tree • Artificial intelligence (AI) • Naive bayes

Machine Learning Approach For Diabetes Prediction

International Journal of Information Systems and Computer Sciences, 2019

Diabetes the silent killer which kills part by part of our life Diabetes can strike anyone, from any walk of life. It is not only a disease but also a creator of different kinds of diseases like heart attack, blindness, kidney diseases etc. It is found that in last decades the cases of people living with diabetes jumped almost 350 million worldwide. Early detection of diabetes can reduce the health risk in patients. With rise of new technology like machine learning we have a solution to this issue, a system for predicting the chance of diabetes. Machine learning techniques increase medical diagnosis accuracy and reduce medical cost. This paper aims to predict diabetes via supervised machine learning algorithms such as decision tree. In our work we have used the Pima Indians Diabetes Dataset from UCI Machine Learning Repository.

Prediction of Diabetes using Machine Learning

Diabetes is one of the chronic diseases due to dysfunction in pancreas which causes the formation of less or no Insulin. The main cause of diabetes remains unknown, yet scientists believe that both genetic factors and environmental lifestyle play a major role. Patients suffering from diabetes develop many other diseases such as nerve damage and heart diseases. Hence, detection of this disease at an early stage can prevent complications and reduce several other health issues. In recent years, models have been built with an Area under ROC curve (AUC(ROC)) of 95% for the prediction of diabetes while the work proposed in the paper is to build an efficient machine learning model with an improved AUC(ROC) of 97.53% using feature creation which is a combination of existing features. The model is deployed over the cloud which improves accessibility with an easier user interface (UI). The model was tested and implemented on Jupyter Notebooks using Python with the usage of Pima Indian diabetes dataset given by the National Institute of Diabetes and Digestive and Kidney Diseases.

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.

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.

An efficient prediction system for diabetes disease based on machine learning algorithms

Data & metadata, 2023

Diabetes is a persistent medical condition that arises when the pancreas loses its ability to produce insulin or when the body is unable to utilize the insulin it generates effectively. In today's world, diabetes stands as one of the most prevalent and, unfortunately, one of the deadliest diseases due to certain complications. Timely detection of diabetes plays a crucial role in facilitating its treatment and preventing the disease from advancing further. In this study, we have developed a diabetes prediction model by leveraging a variety of machine learning classification algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to determine which algorithm yields the most accurate predictive outcomes. we employed the famous PIMA Indians Diabetes dataset, comprising 768 instances with nine distinct feature attributes. The primary objective of this dataset is to ascertain whether a patient has diabetes based on specific diagnostic metrics included in the collection. In the process of preparing the data for analysis, we implemented a series of preprocessing steps. The evaluation of performance metrics in this study encompassed accuracy, precision, recall, and the F1 score. The results from our experiments indicate that the K-nearest neighbors' algorithm (KNN) surpasses other algorithms in effectively differentiating between individuals with diabetes and those without in the PIMA dataset.