scholarly journals Prediction and Classification of Low Birth Weight Data Using Machine Learning Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 18 ◽  
Author(s):  
Alfensi Faruk ◽  
Endro Setyo Cahyono
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Binary Logistic Regression ◽  
Machine Learning Techniques ◽  
Binary Logistic Regression Model ◽  
Data Set ◽  
Learning Techniques

Machine learning (ML) is a subject that focuses on the data analysis using various statistical tools and learning processes in order to gain more knowledge from the data. The objective of this research was to apply one of the ML techniques on the low birth weight (LBW) data in Indonesia. This research conducts two ML tasks; including prediction and classification. The binary logistic regression model was firstly employed on the train and the test data. Then; the random approach was also applied to the data set. The results showed that the binary logistic regression had a good performance for prediction; but it was a poor approach for classification. On the other hand; random forest approach has a very good performance for both prediction and classification of the LBW data set

scholarly journals Factors That Can Trigger Depression

2021 ◽  
Vol 7 (2) ◽  
pp. 164-185
Author(s):  
Haydée Maria Correia da Batista ◽  
Andrea Borges Paim ◽  
Brenda Santos Siqueira ◽  
Nelson Francisco Favilla Ebecken ◽  
Ana Claudia Dias
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
National Health Survey ◽  
Machine Learning Techniques ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables ◽  
Learning Techniques

According to data from the last National Health Survey (PNS), conducted in 2013 by the Brazilian Institute of Geography and Statistics (IBGE) in partnership with the Ministry of Health, 7.6% of people aged 18 and over received diagnosis of depression. Therefore, based on this research, the purpose of this study was to identify factors that may be relevant to a possible diagnosis of depression, using machine learning techniques. The binary logistic regression model was chosen as the machine learning technique, with progressive and regressive methods for selecting variables and a model built by the researcher, generating seven different models. The model’s performance evaluation was made by comparing some metrics such as Cox-Snell R2 and Nagelkerke R2, which presented remarkably close results. Based on these models, 37 explanatory variables were selected which were applied to a new logistic regression model. The results showed that some variables significantly increased the chance of a positive diagnosis of depression as well as some variables were indicative of a reduction in the chances of this diagnosis.

Machine Learning Approaches for Supernovae Classification

2017 ◽  
pp. 207-219 ◽  
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

scholarly journals Classification of Autism Spectrum Disorder Data using Machine Learning Techniques

2019 ◽  
Vol 8 (6S) ◽  
pp. 565-569
Keyword(s):  
Machine Learning ◽  
Autism Spectrum ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Human Communication ◽  
Data Set ◽  
Complex Disorder ◽  
Learning Classifier ◽  
Learning Techniques

Autism is a neuro-developmental disability that affects human communication and behaviour. It is a condition that is associated with the complex disorder of the brain which can lead to significant changes in social interaction and behaviour of a human being.Machine learning techniques are being applied to autism data sets to discover useful hidden patterns and to construct predictive models for detecting its risk.This paper focuses on finding the best machine learning classifier on the UCI autism disorder data set for identifying the main factors associated with autism. The results obtained using Multilayer Perceptron, Naive Bayes Classifier and Bayesian Networkwere compared with J48 Decision tree algorithm. The superiority of MultilayerPerceptron over the well known classification algorithms in predicting the autism risk is established in this paper.

Machine Learning Approaches for Supernovae Classification

2020 ◽  
pp. 294-306
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

Breast Cancer Detection with Revamped Dataset Using Machine Learning Techniques

2021 ◽  
Vol 11 (12) ◽  
pp. 2996-3009
Author(s):  
Sundarambal Balaraman ◽  
Ramesh Ramamoorthy ◽  
Raja Krishnamoorthi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Cancer Data ◽  
Learning Techniques ◽  
Incidence And Mortality

Machine learning is a current topic of interest in research and industry, with the implementation of novel strategies all the time. The main purpose of this research activity is to determine the efficiency of machine learning techniques in the detection research of breast cancer. The incidence and mortality of breast cancer in women are increasing day by day. Worldwide, researchers have worked hard to help clinicians provide the best model for detecting diagnosis and breast cancer. In this work, learning UCI machine Wisconsin breast cancer data from a set of databases, model, and analyze the performance of existing work use, compared to the same data set. The dataset is analyzed, and the revamped dataset is constructed by eliminating redundant features and appending new features essential for prediction. Logistic regression, K nearest neighbors (KNN), support vector machine (SVM), decision trees, random forest, XGBoost, using a machine learning algorithm, such as re-organized data set of artificial neural network AdaBoost, 8 one of prediction build the model application (ANN). Standard to analyze the accuracy rate. In the experiment, these classifications have been shown to work for breast cancer with >97% accuracy. Logistic regression, XGBoost and Adaboost, stand on top with 99.28 percent accuracy. The experiment also, the balanced data set of removal outliers and balance, shows that have a significant impact on the model’s prediction performance.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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