Heart Disease Prediction using Exploratory Data Analysis

Indrakumari, R.; Poongodi, T.; Jena, Soumya Ranjan

doi:10.1016/j.procs.2020.06.017

Cited by 83 publications

(34 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous related studies applied different methods for labeling and compiling datasets, as shown in Tables 2 and 3 . In this study, we followed the most frequently used annotation strategy described in the literature (Ibrohim & Budi, 2018 ; Rezvan et al, 2018 ). We asked a judgment group of five Arabic native speakers to label the tweets manually.…”

Section: Methodsmentioning

confidence: 99%

The design, construction and evaluation of annotated Arabic cyberbullying corpus

2022

View full text Add to dashboard Cite

Cyberbullying (CB) is classified as one of the severe misconducts on social media. Many CB detection systems have been developed for many natural languages to face this phenomenon. However, Arabic is one of the under-resourced languages suffering from the lack of quality datasets in many computational research areas. This paper discusses the design, construction, and evaluation of a multi-dialect, annotated Arabic Cyberbullying Corpus (ArCybC), a valuable resource for Arabic CB detection and motivation for future research directions in Arabic Natural Language Processing (NLP). The study describes the phases of ArCybC compilation. By way of illustration, it explores the corpus to discover strategies used in rendering Arabic CB tweets pulled from four Twitter groups, including gaming, sports, news, and celebrities. Based on thorough analysis, we discovered that these groups were the most susceptible to harassment and cyberbullying. The collected tweets were filtered based on a compiled harassment lexicon, which contains a list of multi-dialectical profane words in Arabic compiled from four categories: sexual, racial, physical appearance, and intelligence. To annotate ArCybC, we asked five annotators to classify 4,505 tweets into two classes manually: Offensive/non-Offensive and CB/non-CB. We conducted a rigorous comparison of different machine learning approaches applied on ArCybC to detect Arabic CB using two language models: bag-of-words (BoW) and word embedding. The experiments showed that Support Vector Machine (SVM) with word embedding achieved an accuracy rate of 86.3% and an F1-score rate of 85%. The main challenges encountered during the ArCybC construction were the scarcity of freely available Arabic CB texts and the deficiency of annotating the texts.

show abstract

Section: Methodsmentioning

confidence: 99%

The design, construction and evaluation of annotated Arabic cyberbullying corpus

2022

View full text Add to dashboard Cite

show abstract

“…An exploratory analysis of the variables is carried out to analyze data using a multivariate method. According to [15], This process will allow mainly enhances and identifies the insight into our dataset, determines the correlation among the explanatory variables, identifies the anomalies and builds models. As a result of this analysis, the following is highlighted:…”

Section: Data Processingmentioning

confidence: 99%

Data Mining to Predict COVID-19 Patients’ Recovery on a Balanced Dataset

Valdiviezo-Díaz

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

The coronavirus disease , has caused a considerable increase in hospitalizations of people with different symptoms caused by this disease. Currently, the world needs a quick solution to tackle the further spread of COVID-19. Data mining techniques, machine learning and other artificial intelligence techniques can provide a best patient prognosis infected by coronavirus. This paper applies data mining techniques to predict COVID-19 infected patients' recovery using open dataset with day level information on COVID-19 affected cases of China. We also use minority Downsampling technique to balance the classes we have in the dataset and thus demonstrate the importance of balancing the classes to yield better results. Additionally, the pre-processing methods and the prediction performance using evaluation metrics are presented. Logistic Regression, Decision tree, and Neural Network algorithms are applied directly on the dataset using R programming language. Experimental results show that the neural network provides a lower error and increases the classification accuracy significantly compared with other algorithms.

show abstract

“…In the other research, Indrakumari and their colleague using exploratory data analysis to predict heart disease, and they have used big data to gain the information, pattern, and knowledge for decision making [6]. The Exploratory Data Analysis (EDA) can be used to detects some mistakes, find appropriate data, assumptions, and determines the correlations.…”

Section: Related Workmentioning

confidence: 99%

“…The exploratory data analysis we will describes about the statistical and graphical or non-graphical using categories in the dataset. Furthermore, EDA was used to analyze the data with advanced technique to gain information such us expose hidden structure, enhances the insight, identifies anomalies, and builds parsimonious model to test the underlaying assumptions [6]. In addition, we will provide to calculate the statistical formulation such as mean, standard deviation, and quartile.…”

Section: B Exploratory Data Analysismentioning

confidence: 99%

Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease

Riyantoko

Fahrudin

Hindrayani

et al. 2021

Int. Intell. Research.

View full text Add to dashboard Cite

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.

show abstract

Heart Disease Prediction using Exploratory Data Analysis

Cited by 83 publications

References 7 publications

The design, construction and evaluation of annotated Arabic cyberbullying corpus

The design, construction and evaluation of annotated Arabic cyberbullying corpus

Data Mining to Predict COVID-19 Patients’ Recovery on a Balanced Dataset

Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease

Contact Info

Product

Resources

About