scholarly journals Performance evaluation of random forest with feature selection methods in prediction of diabetes

2020 ◽  
Vol 10 (1) ◽  
pp. 353
Author(s):  
Raghavendra S ◽  
Santosh Kumar J
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Random Forest ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Selection Methods ◽  
Forward Selection ◽  
Data Set ◽  
Prediction Of Diabetes

<p>Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.</p>

Feature Selection Algorithms for Classification and Clustering

2017 ◽  
pp. 143-167
Author(s):  
Arvind Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Filter Method ◽  
Selection Methods ◽  
Wrapper Method ◽  
Embedded Method ◽  
Selection Algorithms

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.

Feature Selection Algorithms for Classification and Clustering

2020 ◽  
pp. 422-442
Author(s):  
Arvind Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Filter Method ◽  
Selection Methods ◽  
Wrapper Method ◽  
Embedded Method ◽  
Selection Algorithms

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.

Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeepkumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Chronic Disease ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Experimental Result ◽  
Data Set ◽  
Content Type ◽  
Learning Classifier ◽  
Feature Selection Approach

Purpose According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease. Design/methodology/approach A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases. Findings The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6. Originality/value The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

scholarly journals To use or not to use: Feature selection for sentiment analysis of highly imbalanced data

2017 ◽  
Vol 24 (1) ◽  
pp. 3-37 ◽  
Author(s):  
SANDRA KÜBLER ◽  
CAN LIU ◽  
ZEESHAN ALI SAYYED
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Binary Classification ◽  
Small Subset ◽  
Large Set ◽  
Learning Approaches ◽  
Selection Methods ◽  
Data Set

AbstractWe investigate feature selection methods for machine learning approaches in sentiment analysis. More specifically, we use data from the cooking platform Epicurious and attempt to predict ratings for recipes based on user reviews. In machine learning approaches to such tasks, it is a common approach to use word or part-of-speech n-grams. This results in a large set of features, out of which only a small subset may be good indicators for the sentiment. One of the questions we investigate concerns the extension of feature selection methods from a binary classification setting to a multi-class problem. We show that an inherently multi-class approach, multi-class information gain, outperforms ensembles of binary methods. We also investigate how to mitigate the effects of extreme skewing in our data set by making our features more robust and by using review and recipe sampling. We show that over-sampling is the best method for boosting performance on the minority classes, but it also results in a severe drop in overall accuracy of at least 6 per cent points.

scholarly journals Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†

2021 ◽  
Vol 8 (3) ◽  
pp. 209-221
Author(s):  
Li-Li Wei ◽  
Yue-Shuai Pan ◽  
Yan Zhang ◽  
Kai Chen ◽  
Hao-Yu Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Random Forest Regression ◽  
Data Set

Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

scholarly journals Classification of Diabetes using Random Forest with Feature Selection Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1295-1300 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Electronic Health Records ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Health Records

Diabetes has become a serious problem now a day. So there is a need to take serious precautions to eradicate this. To eradicate, we should know the level of occurrence. In this project we predict the level of occurrence of diabetes. We predict the level of occurrence of diabetes using Random Forest, a Machine Learning Algorithm. Using the patient’s Electronic Health Records (EHR) we can build accurate models that predict the presence of diabetes.

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

2019 ◽  
Vol 5 (2) ◽  
pp. 108-119
Author(s):  
Yeslam Al-Saggaf ◽  
Amanda Davies
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Network ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Content Type ◽  
The Social ◽  
Twitter Users

Purpose The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. Design/methodology/approach To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Practical implications Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. Originality/value In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

scholarly journals Phishing Detection Based on Machine Learning and Feature Selection Methods

2019 ◽  
Vol 13 (12) ◽  
pp. 171 ◽  
Author(s):  
Mohammad Almseidin ◽  
AlMaha Abu Zuraiq ◽  
Mouhammd Al-kasassbeh ◽  
Nidal Alnidami
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Selection Methods ◽  
Random Forest Algorithm ◽  
Phishing Detection ◽  
Enormous Number

With increasing technology developments, the Internet has become everywhere and accessible by everyone. There are a considerable number of web-pages with different benefits. Despite this enormous number, not all of these sites are legitimate. There are so-called phishing sites that deceive users into serving their interests. This paper dealt with this problem using machine learning algorithms in addition to employing a novel dataset that related to phishing detection, which contains 5000 legitimate web-pages and 5000 phishing ones. In order to obtain the best results, various machine learning algorithms were tested. Then J48, Random forest, and Multilayer perceptron were chosen. Different feature selection tools were employed to the dataset in order to improve the efficiency of the models. The best result of the experiment achieved by utilizing 20 features out of 48 features and applying it to Random forest algorithm. The accuracy was 98.11%.

A Feature Selection Approach for Network Intrusion Classification

2013 ◽  
Vol 3 (4) ◽  
pp. 51-59 ◽  
Author(s):  
Heba F. Eid ◽  
Mostafa A. Salama ◽  
Aboul Ella Hassanien
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Local Maximum ◽  
Selection Methods ◽  
Hybrid Filter ◽  
Data Set ◽  
Network Intrusion ◽  
Selection Approach ◽  
Feature Selection Approach

Feature selection is a preprocessing step to machine learning, leads to increase the classification accuracy and reduce its complexity. Feature selection methods are classified into two main categories: filter and wrapper. Filter methods evaluate features without involving any learning algorithm, while wrapper methods depend on a learning algorithm for feature evaluation. Variety hybrid Filter and wrapper methods have been proposed in the literature. However, hybrid filter and wrapper approaches suffer from the problem of determining the cut-off point of the ranked features. This leads to decrease the classification accuracy by eliminating important features. In this paper the authors proposed a Hybrid Bi-Layer behavioral-based feature selection approach, which combines filter and wrapper feature selection methods. The proposed approach solves the cut-off point problem for the ranked features. It consists of two layers, at the first layer Information gain is used to rank the features and select a new set of features depending on a global maxima classification accuracy. Then, at the second layer a new subset of features is selected from within the first layer redacted data set by searching for a group of local maximum classification accuracy. To evaluate the proposed approach it is applied on NSL-KDD dataset, where the number of features is reduced from 41 to 34 features at the first layer. Then reduced from 34 to 20 features at the second layer, which leads to improve the classification accuracy to 99.2%.

An Empirical Evaluation of Feature Selection Methods

2015 ◽  
pp. 233-258 ◽  
Author(s):  
Mohsin Iqbal ◽  
Saif Ur Rehman ◽  
Saira Gillani ◽  
Sohail Asghar
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Information Gain ◽  
Learning Algorithm ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Selection Methods ◽  
The One ◽  
Processing And Storage

The key objective of the chapter would be to study the classification accuracy, using feature selection with machine learning algorithms. The dimensionality of the data is reduced by implementing Feature selection and accuracy of the learning algorithm improved. We test how an integrated feature selection could affect the accuracy of three classifiers by performing feature selection methods. The filter effects show that Information Gain (IG), Gain Ratio (GR) and Relief-f, and wrapper effect show that Bagging and Naive Bayes (NB), enabled the classifiers to give the highest escalation in classification accuracy about the average while reducing the volume of unnecessary attributes. The achieved conclusions can advise the machine learning users, which classifier and feature selection methods to use to optimize the classification accuracy, and this can be important, especially at risk-sensitive applying Machine Learning whereas in the one of the aim to reduce costs of collecting, processing and storage of unnecessary data.

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