A Diabetic Disease Prediction Model Based on Classification Algorithms (original) (raw)

Prediction Model for Diabetes Mellitus Using Machine Learning Techniques

— In today " s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

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.

IJERT-Study of Diabetes Prediction using Feature Selection and Classification

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/study-of-diabetes-prediction-using-feature-selection-and-classification https://www.ijert.org/research/study-of-diabetes-prediction-using-feature-selection-and-classification-IJERTV3IS20517.pdf Diabetes mellitus is one of the most serious health challenges in both developing and developed countries. It has become leading cause of death. Detection of diabetes with optimal cost and better performance is the need of the age. Medical data are multidimensional; hence data pre-processing step is applied to high dimensional data. Feature selection is a pre-processing step that is applied to high dimensional dataset to reduce number of dimensions by selecting the most informative features that influence the diagnosis of the disease. The Pima Indian diabetic database at the UCI machine learning laboratory has become a standard dataset for testing data mining algorithms to see their prediction accuracy in diabetes data classification. The F-score feature selection method and k-means clustering select the optimal feature subsets of the medical datasets that enhances the performance of the Support Vector Machine classifier. The performance of the SVM classifier is empirically evaluated on the reduced feature subset of Diabetes dataset. Then performance is validated using four parameters namely the Accuracy of the classifier, Area Under ROC (Receiver operating characteristics) Curve, Sensitivity and Specificity.

Diabetic Disease Prediction System using Supervised Machine Learning Approaches

IJCSE, 2021

In the present study Diabetics is one of the critical diseases which can fall at any group of age and gender. The major causes lead to diabetics is mostly inheritance, in a proper healthy lifestyle, Irregular food habits, stress, and no physical exercise. Prediction of Diabetics is a very important study since it is one of the leading causes of sudden kidney failures, heart attacks, and brain stroke etc. The diabetic patient treatment can be done through patient health history. The Doctor can find hidden information about the patient through healthcare applications and it will be used for effective decision-making for the patient"s health condition. The healthcare industry is also collecting a large amounts of patient health information from different data warehouses. Using these healthcare databases researchers used to extract information for predicting the diabetics of the patient. Researchers are focused on developing software with the help of machine learning methods that can help clinicians to make better decisions about a patient's health based on their prediction and diagnosis. The main purpose of this program is to diagnose a patient's diabetes using machine learning methods. A relative study of the various competences of machine learning approaches will be done through a graphical representation of the results. The goal and objective of this project is to predict the chances of diabetics then provide early treatment to patients, which will reduce the life-risk and cost of treatment. For this purpose a probability modeling and machine learning approach like Support Vector Machine algorithm Decision tree algorithm, Naive Bayes algorithm, Logistic regression algorithm are used to predict diabetics.

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.

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.

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.

Classification of Diabetes Patient Using Machine Learning Approaches

2017

The issues of lifestyle disease are paramount and Diabetes is among them which is estimated to cause 1.5-5 million deaths per year. It affects around 422 million people worldwide. The number is likely to double within 20 years. Due to its importance, a better approach for the detection of Diabetes disease with optimal cost and performance is the need of the age. Fortunately many diabetes cases can be prevented or avoided by just improved awareness and lifestyle adjustments. So, early diagnosis of the disease is necessary for better chance of recovery. In this project, we tried to build a classifier that classifies a patient having diabetes based on some attributes of the patient by using five machine learning computational methods namely K-Nearest Neighbour (K-NN), Backpropagation algorithm, Decision Tree method, Naive Bayes Classifier and Support Vector Machine (SVM). We have applied all these algorithms on Pima-Indians-diabetes dataset which is available at the UCI machine learnin...

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.