scholarly journals An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction

2021 ◽  
Vol 16 (4) ◽  
pp. 1-12
Author(s):  
Mohammed Alghobiri ◽  
Hikmat Ullah Khan ◽  
Ahsan Mahmood
Keyword(s):  
Liver Disease ◽  
Human Body ◽  
Regression Models ◽  
Near Neighbor ◽  
The Body ◽  
Machine Learning Techniques ◽  
Logistic Regression Models ◽  
Learning Techniques ◽  
Tenfold Cross Validation ◽  
Day By Day

The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.

scholarly journals Performance Analysis of Diabetes Mellitus Using Machine Learning Techniques

2021 ◽  
Vol 12 (6) ◽  
pp. 225-230
Author(s):  
Kandala Srujana Kumari Et.al
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Near Neighbor ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Training Methods ◽  
Learning Techniques ◽  
Many Body Systems

Diabetes is a common disease in the human body caused by a set of metabolic disorders in which blood sugar levels are very long. It affects various organs in the human body and destroys many-body systems, especially the kidneys and kidneys. Early detection can save lives. To achieve this goal, this study focuses specifically on the use of machine learning techniques for many risk factors associated with this disease. Technical training methods achieve effective results by creating predictive models based on medical diagnostic data collected on Indian sugar. Learning from such data can help in predicting diabetics. In this study, we used four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), Near Neighbor K (KNN), and Decision Tree C4.5 (DT), based on statistical data. people. adults in sugar. , preview. The results of our experiments show that the C4.5 solution tree has greater accuracy compared to other machine learning methods.

scholarly journals Machine learning techniques to derive bioclimatic classifications for Colombia

2021 ◽  
Author(s):  
Richard Rios ◽  
Elkin A. Noguera-Urbano ◽  
Jairo Espinosa ◽  
Jose Manuael Ochoa
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Regression Models ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Study Region ◽  
Logistic Regression Models ◽  
Learning Techniques

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We firstly characterized the precipitation space of Colombia using principal component analysis. Based on Lang classification, we then projected all background sites in the precipitation space with their corresponding categories. We sequentially fit logistic regression models to re-classify all background sites in the precipitation space with six redefined Lang categories. New categories were the used to define a new modified Lang and Caldas-Lang classifications.

Beverage intake and obesity in early childhood: evidence form primary health care clients in Northwest Argentina

2015 ◽  
Vol 7 (3) ◽  
pp. 244-252 ◽  
Author(s):  
E. Alderete ◽  
I. Bejarano ◽  
A. Rodríguez
Keyword(s):  
Public Health ◽  
Nutritional Status ◽  
Regression Models ◽  
Task Force ◽  
Normal Weight ◽  
The Body ◽  
Logistic Regression Models ◽  
Dietary Recall ◽  
Northwest Argentina ◽  
Artificial Fruit

Sugar sweetened beverages (SSB) are thought to play an important role in weight gain. We examined the relationship between the intake of caloric and noncaloric beverages (SSB and water) and the nutritional status of children. In 2014, we randomly selected 16 public health clinics in four cities of Northwest Argentina and conducted a survey among mothers of children 0–6 years of age. Children’s beverage intake was ascertained by 24-h dietary recall provided by the mothers. Children’s weight and height measures were obtained from clinic’s registries. We calculated the body mass index using the International Obesity Task Force standards. The analysis included 562 children 25 months to 6 years of age with normal or above normal nutritional status. Children’s beverage consumption was as follows, water 81.8%, carbonated soft drinks (CSD) 49.7%, coffee/tea/cocoa 44.0%, artificial fruit drinks 35.6%, flavored water 17.9%, natural fruit juice 14.5%. In multivariate logistic regression models the likelihood of being obese v. being overweight or having normal weight doubled with an intake of one to five glasses of CSD (OR=2.2) and increased by more than three-fold with an intake of more than five glasses (OR=3.5). Drinking more than five glasses of water decreased the likelihood of being obese by less than half (OR=0.3). The percentage of children drinking more than five glasses of other beverages was low (3.3–0.9%) and regression models did not yield significant results. The study contributed evidence for reducing children’s CSD intake and for promoting water consumption, together with the implementation of comprehensive regulatory public health policies.

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