Predictive machine learning model for early detection and analysis of diabetes

2020 ◽  
Author(s):  
D. Jashwanth Reddy ◽  
B. Mounika ◽  
S. Sindhu ◽  
T. Pranayteja Reddy ◽  
N. Sagar Reddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images

2021 ◽  
pp. 104838
Author(s):  
Amith Khandakar ◽  
Muhammad.E.H. Chowdhury ◽  
Mamun Bin Ibne Reaz ◽  
Sawal Hamid Md Ali ◽  
Md Anwarul Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Diabetic Foot ◽  
Learning Model ◽  
Machine Learning Model

A novel work on analyzing Stress and Depression level of Indian population during COVID-19

2020 ◽  
Vol 13 ◽  
Author(s):  
Amit Kumar Gupta ◽  
Priya Mathur ◽  
Shruti Bijawat ◽  
Abhishek Dadhich
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Age Groups ◽  
Multiple Linear Regression Model ◽  
Learning Model ◽  
The People ◽  
Machine Learning Model ◽  
The World ◽  
Depression Level ◽  
Detection Of Stress

Objective: The world is facing the pandemic situation of COVID-19 which leads to a large level of stress and depression on mankind as well on society. Static measurements can be conducted for early identification of the stress and depression level and diagnose or preventing from the effect of these conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression of humankind by statistically analyzing the behavior of humankind which helps to the early detection of stress and depression. This helps to prevent society from psychological pressures from any disaster like COVID-19. The COVID-19 pandemic is one of the public health emergencies which are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the disaster caused by this disease. Methods: In this paper, the authors have surveyed by defining some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the situation COVID19The data sets are analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression is mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The Machine learning model is the best way to predict the level of stress and depression of humankind by statistically analyzing the behavior of humankind which helps the early detection of stress and depression. Results: Females are more stressed and depressed than males. The people who are 45+ years age are more stressed and depressed. The male and female students are more stressed and depressed. The overall analysis said that the peoples of India are stressed and depressed at the level of “Serve” due to COVID-19. This can because of a student’s career concerning their study and examination. The females who feel so much burden of business as well as their salary. The aged people are depressed due to COVID-19 disaster. Conclusion: This research given very big support to understand our objectives. We have also implemented our analysis of data based on DASS-21 parameters defined for the Anxiety, Depression, and stress at the world level. By the analysis defined in section 5 we conclude that the people of India are more stressed and depressed at the level of "Serve" due to COVID-19.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model
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