Early Stage Prediction of Diabetes Using Machine Learning Techniques

2022 ◽  
pp. 310-317
Author(s):  
Bikash Kumar Sahu ◽  
Nimisha Ghosh
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3449-3452
Author(s):  
M. S. Roobini ◽  
Y. Sai Satwick ◽  
A. Anil Kumar Reddy ◽  
M. Lakshmi ◽  
D. Deepa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
The Body ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Pima Indians ◽  
Classification Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

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.

Early Stage Detection of Heart Failure Using Machine Learning Techniques

2021 ◽  
pp. 75-88
Author(s):  
Zulfikar Alom ◽  
Mohammad Abdul Azim ◽  
Zeyar Aung ◽  
Matloob Khushi ◽  
Josip Car ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques

Cataloguing of Attention Deficit Hyperactivity Disorder (ADHD) among Children

2021 ◽  
Author(s):  
Sumathi M ◽  
Dr. G S Mamatha ◽  
Dr. Ramaa A
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Medical Field ◽  
Hyperactivity Disorder ◽  
Learning Techniques ◽  
Chronic Disorder ◽  
Adhd Children ◽  
Anxiety Depression ◽  
Treatment Rules

<p>Children are the dream of parents. Children ADHD is a bygone and chronic disorder which leads to problems in children. If not solved in childhood stages will continue in future till adolescents. The disorder consequences are difficulty to study the tasks which are related to anxiety, depression and other psychological problems. Hence the disorder must be resolved in the early stage to control any type of consequences in future for our children. The medical field is an eminent area in today’s world such as signal processing, Imaging, MRI, EEG etc. to diagnose and offer treatment. Even technology field too contributing to ADHD children by providing different techniques in different areas such as IoT, mobile, Robot, Application, virtual reality, augmented reality, machine learning techniques etc. to give diagnosis and treatment methods. The paper reviews and summarizes the set of features, diagnosis methods, treatment rules for ADHD children.</p>

scholarly journals Effective Prediction of Diabetes Mellitus using Nine different Machine Learning Techniques and their Performances

2020 ◽  
Vol 9 (5) ◽  
pp. 439-445
Keyword(s):  
Machine Learning ◽  
Blood Sugar ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Machine Learning Techniques ◽  
High Blood Sugar ◽  
Learning Techniques ◽  
Positive Rate ◽  
Logical Regression ◽  
Prediction Of Diabetes

Diabetes is a disease where the predominant finding is high blood sugar. The high blood sugar may either be because of deficient insulin production (Type 1) or insulin resistance in peripheral tissue cells (Type 2). Many problems occur if diabetes remains untreated and unidentified. It is additional inventor of various varieties of disorders for example: coronary failure, blindness, urinary organ diseases etc. Nine different machine learning techniques are used in this research work for prediction of diabetes. A dataset of diabetic patient’s is taken and nine different machine learning techniques are applied on the dataset. Positive likelihood ratio, Negative likelihood ratio, Positive predictive value, Negative predictive value, Disease prevalence, Specificity, Precision, Recall, F1-Score ,True positive rate, False positive rate of the applied algorithms is discussed and compared. Diabetes is growing at an increasing in the world and it requires continuous monitoring. To check this we use Logical regression, Random forest, Logical regression CV, Support Vector Machine, Artificial Neural Network (ANN), Decision Tree, k-nearest neighbors (KNN), XGB classifier.

scholarly journals ANALYSIS OF ALZHEIMER DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 519-525
Author(s):  
B. Hemalatha, Dr. M. Renukadevi
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Know How ◽  
Positron Emission ◽  
Learning Techniques ◽  
The Many

Alzheimer's Disease (AD) is referred to as one of the highest non-unusual neurodegenerative disorders that inflict eternal harm to the memory-associated brain cells and wonder skills. There is a 99.6 percent failure rate in clinical trials of Alzheimer's disease pills, perhaps due to the fact that AD sufferers cannot be without early-stage complications. This observation analyzed machine learning knowledge of strategies to use empirical statistics to forecast the progression of AD in the years of fate. Diagnosis of AD is often difficult, particularly at an early stage in the disease system, due to the degree of mild cognitive impairment (MCI). However, it is at this point where treatment is much more likely to be successful, so there will be great benefits in enhancing the diagnosis process. Research in this area aims to identify the most complex mechanisms directly related to changes in AD. Various imaging methods are used to diagnose AD, and image modes play a key role in the diagnosis of AD. This paper uses a Positron Emission Tomography (PET) image to detect AD early. The PET image is often used to know how organs and tissues function in the human body. This research study analyses prediction approaches using various kinds of machine learning algorithms to solve AD diagnostic problems. Artificial Neural Networks are one of the many algorithms. Modern research has shown that deep learning is a proficient technique for solving numerous problems of image recognition, but most of these published approaches owe their performance to training on a very large number of data samples.

scholarly journals A Survey on Data Mining Techniques for COVID Prediction

2021 ◽  
Vol 9 (8) ◽  
pp. 1051-1056
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Virus Disease ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Loss Of Life ◽  
Data Mining Techniques ◽  
Medical Sector ◽  
Learning Techniques ◽  
Mining Methods

Corona Virus Disease of 2019 (COVID-19) has emerged as a serious health emergency worldwide. The symptoms of COVID-19 are un-detectable at early stage in most of the patients. It spreads from person to person very rapidly and causes severe sickness and loss of life in a number of cases if not treated early. Data mining techniques are very commonly being used in medical sector for detection and prediction of a variety of diseases and medical conditions of patients. A number of researchers are also working towards prediction of possibility of infection of COVID-19 among humans using machine learning techniques, specifically by applying data mining methods. In this paper, an extensive survey of available literature in the domain of prediction of COVID-19 infection and other diseases has been presented. This also includes survey on data mining techniques, models and various datasets.

scholarly journals Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 28-33
Author(s):  
O. Kurasova ◽  
◽  
V. Marcinkevičius ◽  
V. Medvedev ◽  
B. Mikulskienė
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Early Design Stage ◽  
Furniture Manufacturing ◽  
Learning Techniques ◽  
Key Factor ◽  
The Cost

Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.

Sign in / Sign up

Export Citation Format

Share Document