scholarly journals Disease Prediction for the Deprived using Machine Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 404-408
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Chronic Disease ◽  
Social Welfare ◽  
Early Stage ◽  
Disease Prediction ◽  
Disease Diagnostics ◽  
The One ◽  
The Cost ◽  
Deficiency Diseases

Our work aims for economical disease diagnostics, by asking the user for Prognosis and symptoms, accurate disease prediction has been strived for. In aspiration for social welfare, the cost of using the product built is almost free, the prediction can be done using any one of the six algorithms, five out of which are total free of cost for use, those five being KNN, Naïve Bayes, SVM , Logistic Regression, K Means Classifier. The one, that gives out predictions with most accuracy, i.e., Decision Trees Classifier, has been made paid, others are not to be paid for, for using.How this product would be functioning is simple: User logs in , openCV has been used for it, that brings the user to the section where user is briefed about models working on different algorithms, each algorithm having different accuracy, thus further, which model he/ she should choose. On choosing model of their choice, they fill their symptoms and prognosis, that yields them their final result of name of their disease.Services like these are greatly needed , looking at large many number of people in our society, who are unfortunately not able to afford them, when priced heavily, or even moderately. Such products can help save many a lives, notify sufferer about his chronic disease at early stage, inform about deficiency diseases, that are very controllable, if get known about, early.

scholarly journals Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 127-133
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Heart Diseases ◽  
Experimental Results ◽  
Disease Prediction ◽  
Feature Importance ◽  
The World ◽  
Feature Scaling ◽  
Logistic Regression Classifier

In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.

Preliminary application of machine learning in ensemble forecasting

2020 ◽  
Author(s):  
Junjie Ma ◽  
Wansuo Duan
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Weather Prediction ◽  
Ensemble Methods ◽  
Ensemble Forecasting ◽  
Optimal Perturbation ◽  
Average Value ◽  
Analysis Error ◽  
The Cost ◽  
Historical Samples

<p>The optimal perturbation method is a beneficial way to generate ensemble members to be used in ensemble forecasting. With orthogonal optimal perturbation, orthogonal conditional nonlinear optimal perturbations (O-CNOPs) generating initial perturbations and orthogonal nonlinear forcing singular vectors (O-NFSVs) generating model perturbations are two kinds of skillful ensemble forecasting methods. There is main disadvantage that O-CNOPs and O-NFSVs generate optimal perturbation members may need a lot of time, but in practical weather prediction, the ensemble members usually need to be generated quickly. In order to benefit from O-CNOPs and O-NFSVs, as well as considering the cost of calculation, therefore, we present a way with the big data and machine learning thinking to simplify the process of the optimal perturbation ensemble methods. Using the historical samples and their optimal perturbations to establish a database, we look for the historical sample which is analogous to what need to be forecasted currently from the database by using the convolutional neural network (CNN). In comparison with using optimization algorithm to get O-CNOPs and O-NFSVs directly, this way gets O-CNOPs and O-NFSVs faster which still obtain acceptable prediction performance. In addition, once the CNN model is trained completely, the cost of time for prediction will be saved. We illustrate the advantage by numerical simulations of a Lorenz 96 model.</p><p>Further more, based on above study, some comparison of the ensemble forecasting skill of O-CNOPs and O-NFSVs has been done, and there are three results for the reference: (1) in the early stage (1-6 days), the O-CNOPs method perform more skillfully, and in the later stage (6-12 days), the O-NFSVs method perform more skillfully; (2) within 1-5 days, if the development of analysis error is bigger than or close to the average value of the analysis error development of historical samples, the O-CNOPs method is preferred, else the O-NFSVs method is preferred; (3) within 0-3 days, if the development of energy is bigger than or close to the average value of the energy development of the historical samples, the O-CNOPs method is preferred, else the O-NFVS method is preferred. Next, further work is required to examine and explore more and deeper research using machine learning in ensemble forecasting studies of atmosphere and other systems.</p>

scholarly journals Disease Prediction and Doctor Recommendation System using Machine Learning Approaches

2021 ◽  
Vol 9 (VII) ◽  
pp. 34-44
Author(s):  
Anand Kumar
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Recommendation System ◽  
Disease Patient ◽  
Classification Algorithms ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Interactive Interface ◽  
Front End ◽  
Doctor Recommendation

In our everyday life we go over numerous individuals who are experiencing some sort of Diseases. Prediction of disease is an integral part of treatment. In this project the disease is accurately predicted by looking at the symptoms of the patient where the patient can input his/her symptom and the system will predict the disease patient is suffering from. Classification Algorithms like the Naïve Bayes (NB), Random Forest, Logistic Regression and KNN have been broadly utilized to anticipate the Disease, where different accuracies were obtained. In corresponding to a particular Disease, for example, Heart Disease, Diabetes and so on is additionally anticipated by demonstrating “True” or “False” i.e. if an individual is having or not having that Disease. Prediction of such a system can have a very large potential in the medical treatment of the future. Once the Disease is predicted by the system, It then recommends which type of doctor to consult. In this paper, an audit of some new works identified with utilization of Machine Learning in expectation of disease is predicted. An interactive interface is built as front-end to facilitate interaction with the symptoms. The whole model is implemented using Django and is connected to the Django Server.

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.

Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 373-381
Author(s):  
Shiva Shanta Mani B. ◽  
Manikandan V. M.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Early Stage ◽  
Heart Diseases ◽  
Age Groups ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Health Issues ◽  
Decision Tree Classifier ◽  
Knn Classifier

Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.

scholarly journals A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

2020 ◽  
Vol 10 (5) ◽  
pp. 1874 ◽  
Author(s):  
José M. Bolarín ◽  
F. Cavas ◽  
J.S. Velázquez ◽  
J.L. Alió
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Web Application ◽  
High Performance ◽  
Logistic Regression Model ◽  
Early Stage ◽  
Disease Classification ◽  
Multivariate Logistic Regression Model ◽  
Detection Model

This work pursues two objectives: defining a new concept of risk probability associated with suffering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Different demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms.

scholarly journals Chronic Disease Prediction Using Machine Learning

IJARCCE ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 287-292
Author(s):  
Meghana M ◽  
Shashank S ◽  
Tojo Mathew
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Disease Prediction

scholarly journals Performance Evaluation of Machine Learning Models for Diabetes Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 1976-1980 ◽  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Adult Population ◽  
International Diabetes Federation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Pima Indians ◽  
K Nearest Neighbor

Diabetes is one of the prevalent diseases all over the world. As per the International Diabetes Federation (IDF) report of the year 2017, diabetes is prevalent in about 8.8% of the Indian adult population and is one of the top ten causes of death in India. In untreated and unidentified diabetes could cause fluctuations in the sugar levels and extreme cases, damage organs such as kidneys, eyes, and arteries in the heart. By using Machine learning algorithms to predict the disease from the relevant datasets at an early stage could likely save human lives. The purpose of this investigation is to assess the classifiers that can predict the probability of disease in patients with the greatest precision and accuracy. Experimental work has been carried out using classification algorithms such as K Nearest Neighbor (KNN), Decision Tree(DT), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest(RF) on Pima Indians Diabetes dataset using nine attributes which is available online on UCI Repository. The performance of classifier is evaluated based on precision, recall, accuracy and is estimated over correct and incorrect instances. The results proved that Logistic Regression (LR) performs better with the accuracy of 77.6 % in comparison to other algorithms

Lifetime Well-Being

2016 ◽  
Author(s):  
Gregory Ponthiere
Keyword(s):  
Public Policy ◽  
Social Welfare ◽  
Social Welfare Function ◽  
Well Being ◽  
The Other ◽  
Normative Economics ◽  
Ex Post ◽  
Optimal Policies ◽  
The One ◽  
The Cost

This chapter reviews recent contributions in positive and normative economics concerned with how individuals plan, over their uncertain lifetime, their consumption and health-affecting activities, and with the design of the optimal public policy in that context. The chapter first emphasizes that contemporary theories aimed at explaining how individuals plan their lives rely on unequal forms and degrees of rationality. On the normative side, it argues that there exists a tension between, on the one hand, optimal policies derived from a utilitarian social welfare function, and, on the other hand, optimal policies derived from an ex post egalitarian social welfare function. Actually, optimal policies under utilitarianism—encouraging savings, annuitization, and prevention—increase expected lifetime well-being, but at the cost of reducing the realized lifetime well-being of the unlucky short-lived, which raises inequalities in realized lifetime well-being.

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