scholarly journals Prediction of Liver Disease using Regression Tree

2021 ◽  
Vol 17 (02) ◽  
pp. 164
Author(s):  
Vinutha M.R. ◽  
Chandrika J.
Keyword(s):  
Machine Learning ◽  
Liver Cirrhosis ◽  
Liver Disease ◽  
Decision Trees ◽  
Regression Tree ◽  
Decisive Role ◽  
Integrated System ◽  
Common Disease ◽  
Data Set ◽  
Fully Integrated

<p class="0abstract"><strong>Abstract—</strong><strong> </strong>Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important.The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not.</p><p class="0abstract"> </p>

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Future Prediction of Diabetics using XG Booster Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 2128-2132
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Common Disease ◽  
Data Set ◽  
Glucose Content

Diabetes is a most common disease that occurs to most of the humans now a day. The predictions for this disease are proposed through machine learning techniques. Through this method the risk factors of this disease are identified and can be prevented from increasing. Early prediction in such disease can be controlled and save human’s life. For the early predictions of this disease we collect data set having 8 attributes diabetic of 200 patients. The patients’ sugar level in the body is tested by the features of patient’s glucose content in the body and according to the age. The main Machine learning algorithms are Support vector machine (SVM), naive bayes (NB), K nearest neighbor (KNN) and Decision Tree (DT). In the exiting the Naive Bayes the accuracy levels are 66% but in the Decision tree the accuracy levels are 70 to 71%. The accuracy levels of the patients are not proper in range. But in XG boost classifiers even after the Naïve Bayes 74 Percentage and in Decision tree the accuracy levels are 89 to 90%. In the proposed system the accuracy ranges are shown properly and this is only used mostly. A dataset of 729 patients can be stored in Mongo DB and in that 129 patients repots are taken for the prediction purpose and the remaining are used for training. The training datasets are used for the prediction purposes.

scholarly journals Implementation of Liver Disease Prediction Using Machine Learning

2021 ◽  
pp. 606-616
Author(s):  
Boggarapu Sai Surya ◽  
Nitesh Kumar Singh ◽  
S Sasi Rekha
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Disease Prediction ◽  
Medical Field ◽  
Data Set ◽  
Classification Techniques

This work Liver Disease Prediction Using Machine Learning is a machine learning application. In this project, you predict whether the patient contain a liver disease or not using python Jupyter Notebook. To predict presence of liver disease we apply some of the classification techniques. It gives an idea of how machine learning helps in medical field and how classification techniques going to predict liver disease using liver disease data set.

scholarly journals Global Warming Prediction in India using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 4061-4065
Keyword(s):  
Machine Learning ◽  
Global Warming ◽  
Linear Regression ◽  
Greenhouse Gases ◽  
Learning Algorithm ◽  
Regression Tree ◽  
Global Temperature ◽  
Support Vector ◽  
Data Set ◽  
Average Annual Temperature

Long term global warming prediction can be of major importance in various sectors like climate related studies, agricultural, energy, medical and many more. This paper evaluates the performance of several Machine Learning algorithm (Linear Regression, Multi-Regression tree, Support Vector Regression (SVR), lasso) in problem of annual global warming prediction, from previous measured values over India. The first challenge dwells on creating a reliable, efficient statistical reliable data model on large data set and accurately capture relationship between average annual temperature and potential factors such as concentration of carbon dioxide, methane, nitrous oxide. The data is predicted and forecasted by linear regression because it is obtaining the highest accuracy for greenhouse gases and temperature among all the technologies which can be used. It was also found that CO2 is the plays the role of major contributor temperature change, followed by CH4, then by N20. After seeing the analysed and predicted data of the greenhouse gases and temperature, the global warming can be reduced comparatively within few years. The reduction of global temperature can help the whole world because not only human but also different animals are suffering from the global temperature.

scholarly journals FREQUENCY OF PROLONGED ACTIVATED PARTIAL THROMBOPLASTINE TIME (APTT) IN LIVER CIRRHOSIS.

1969 ◽  
Vol 4 (1) ◽  
pp. 421-425
Author(s):  
MUKAMIL SHAH ◽  
ABDUL AHAD ◽  
IMRAN UD DIN KHATTAK
Keyword(s):  
Liver Cirrhosis ◽  
Liver Disease ◽  
Chronic Liver Disease ◽  
Liver Tissue ◽  
Chronic Liver ◽  
Common Disease ◽  
Significant Finding ◽  
Thrombin Time ◽  
Cross Sectional ◽  
Prolonged Aptt

BACKGROUND: Liver cirrhosis is very common disease in medical practice. Chronic Liver diseaseis marked by the gradual destruction of liver tissues over time. Severe liver disease falls under thiscategory including cirrhosis of liver and fibrosis. Chronic damage to the liver slowly replaces normalfunctioning liver tissue, progressively diminish blood flow though the liver. As the normal liver tissue islost, nutrients, horrmones, drugs and poisons are not processed effectively by the liver. In additionprotein production including coagulation factors and other substances produced by the liver areinhibited. This study was designed to find the relative frequency of having prolonged APTT in chronicliver disease.STUDY DESIGN AND PLACE: This cross-sectional study of chronic liver disease was conducted inthe departments of Medicine and Pathology of Saidu Teaching Hospital / Saidu Medical College, Swat,Pakistan from Feb 15th 2012 to Dec 15th 2012.MATERIAL AND METHODS: Seventy eight (78) patients of chronic liver disease (38 Males & 40Females) were studied. Those on anticoagulants therapy were excluded from the study.Ethyelenediamine tetra acetic acid and citrated blood samples were taken for tests to be performed.Blood counts were performed on coulter counter haematology analyzer. APTT, Prothrombine time (PT),Bleeding time (BT) and thrombin time (TT) were performed manually.RESULTS: Out of 78 patients of chronic liver disease, prolonged APTT was found in 64(82.05%)patients (28 males and 36 females). 14(17.95%) patients (10 males and 4 females) were found to havenormal APTT.CONCLUSION: Prolong APTT is significant finding in cirrhosis liver. It should be included in theinvestigations for bleeding tendencies of cirrhotic patients.KEY WORDS: Activated partial thromboplastin time, chronic liver disease, cirrhosis liver.

scholarly journals Property valuation by machine learning for the Norwegian real estate market

2021 ◽  
Author(s):  
Amandip Sangha
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Large Scale ◽  
State Of The Art ◽  
Regression Tree ◽  
Real Estate Market ◽  
Research Literature ◽  
Data Set ◽  
Boosted Regression Tree ◽  
Market Data

We train a machine learning model on large data set for predicting property values in the Norwegian real estate market. Our model is a gradient boosted regression tree. The data set is the largest market data set of properties in Norway considered in the research literature. We achieve state of the art accuracy. A large scale market data set of real estate properties is collected from sales and rental ads on publicly accessible internet sites. The property advertisements show property features and appraisal values made by real estate brokers. We train a gradient boosted regression tree model on selected features of the data set. This is a multivariate regression model built with supervised learning. We do 5-fold cross validation to assess the accuracy and robustness of the model. The gradient boosted regression tree models are already known to give the best prediction accuracy on real estate price valuations. We achieve state of the art pre- diction accuracy using a minimal feature set and only publicly and freely available sales advertisement data. The novelty of our work lies in the fact that we use a minimal feature set in our model, and we have the largest data set in the research literature, and moreover we have used only freely and publicly accessible data which are simple to obtain. This shows that useful estimation models with high accuracy can be built with quite simple resources.

scholarly journals Classification of Diabetes Mellitus Based on Machine Learning Classifier Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 4680-4684
Keyword(s):  
Machine Learning ◽  
Eating Habits ◽  
Data Cleaning ◽  
The Body ◽  
Common Disease ◽  
Data Set ◽  
Learning Classifier ◽  
Sugar Levels ◽  
Sleeping Habits ◽  
The City

In recent days diabetes is recorded as fastest growingst common disease among several diseases in the world. It became one of the major health problems in several states and countries. This occurs mainly when the normal human body is incapable to produce the sufficient amount of insulin in order to adjust the quantity of sugar levels in the body. This improper maintenance of sugar levels may lead to other diseases like heart disease, kidney disease, blindness, nerve damage and blood vessels damage. every one knows that there are mainly two general reasons for diabetes: One reason is the pancreatic gland not able make sufficient insulin or the body not produce make enough insulin. This type of symptom is mainly found on 5-10 % of citizens with diabetes and they come under Type-1 Diabetes. Another reason is cells do not respond to the insulin that is produced and this type of symptom people come under Type-2 Diabetes. In recent days, machine learning as well as DM techniques have been considered to design automatic diagnosis system for diabetes. In this proposed paper we aim to use the SVM, a ML method as a classifier for identification of diabetes data set. Here we applied the data cleaning techniques to handle the incomplete by fill in absent values, smoothening the noisy data, identified and removed the inconsistencies. By performing the data cleaning activities for verifying all the fields in the data set are properly arranged or not. Once after the data cleaning is completed then we try to apply SVM and Naive Bayes for classifying the diabetes dataset and we try to compare the both classifiers and find which classification techniques are efficient by comparing the both classifiers based on the results we observed. Our experimental results clearly tell that SVM can be effectively used for identifying diabetes disease. In this proposed application we try to analyze the diabetes based on location wise i.e. diabetes is differently affected by various people in and around the various corners of the city. In this proposed application we take sample data set of Visakhapatnam city with four area’s data like NORTH, SOUTH, and WEST & EAST. So by applying the SVM, we will try to analyze the different reasons which differ for each and every diabetes patients based on location wise. some area people mostly suffer with food, pollution, eating habits, daily habits, sleeping habits and other reasons. So based on each and every individual region diabetes differ one with other person in and around the city. So we are going to classify a set of patients data based on region wise and classify the cause of affecting diabetes for them and try to provide a counter measure and pre-cautions for the patients.

scholarly journals Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1643
Author(s):  
Deren Lu ◽  
Zhidong Chen ◽  
Faxing Ding ◽  
Zhenming Chen ◽  
Peng Sun
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Regression Tree ◽  
Predictive Ability ◽  
Data Set ◽  
Comprehensive Data ◽  
Cfst Columns ◽  
Robust Processing ◽  
Data Group ◽  
Stub Columns

In this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjusting the selected parameters indicated in the previous research (B, D, t, ρsa, fcu, fs). The advantage of GBRT is its strong predictive ability, which can naturally handle different types of data and very robust processing of outliers out of space. The comprehensive data set obtained from the FEM method which has been verified the accuracy and rationality by the existing literature. In order to make the data group closer to the engineering example, a large amount of experimental data collected in the literature was added to the data group to enhance the accuracy of the model. We compare a few regression models simply and the results show that the GBRT model has a good predictive effect on the mechanical properties of CFST columns. In summary, it can help pre-investigations for the CFST columns.

FR-IR Molecular Microanalysis System

1990 ◽  
Vol 48 (2) ◽  
pp. 270-271
Author(s):  
John A. Reffner ◽  
William T. Wihlborg
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Integrated System ◽  
Morphological Examination ◽  
Fully Integrated ◽  
Ir Microscopy ◽  
Normal Functions ◽  
Infrared Microspectroscopy ◽  
Infrared Spectral ◽  
Ft Ir

The IRμs™ is the first fully integrated system for Fourier transform infrared (FT-IR) microscopy. FT-IR microscopy combines light microscopy for morphological examination with infrared spectroscopy for chemical identification of microscopic samples or domains. Because the IRμs system is a new tool for molecular microanalysis, its optical, mechanical and system design are described to illustrate the state of development of molecular microanalysis. Applications of infrared microspectroscopy are reviewed by Messerschmidt and Harthcock.Infrared spectral analysis of microscopic samples is not a new idea, it dates back to 1949, with the first commercial instrument being offered by Perkin-Elmer Co. Inc. in 1953. These early efforts showed promise but failed the test of practically. It was not until the advances in computer science were applied did infrared microspectroscopy emerge as a useful technique. Microscopes designed as accessories for Fourier transform infrared spectrometers have been commercially available since 1983. These accessory microscopes provide the best means for analytical spectroscopists to analyze microscopic samples, while not interfering with the FT-IR spectrometer’s normal functions.

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