A study on using Python vs Weka on dialysis data analysis

Mitrpanont, Jarernsri L.; Sawangphol, Wudhichart; Vithantirawat, Thanita; Paengkaew, Sinattaya; Suwannasing, Prameyuda; Daramas, Atthapan; Chen, Yi-Cheng

doi:10.1109/incit.2017.8257883

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…Therefore, in this study, it is proposed to expose, through the use of the WEKA computational software and the application of some clustering models, how it can be contributed through the use of data mining techniques [8], both in the diagnosis of diseases and in the prevention of the causes that could derive in arterial hypertension, where the risk factors of the cause of death in the I10-I15 interval are detected, relating through a process of correlation context variables such as disability, overwork, pregnancy of high risk, stress, high diets, and poor nutrition. Therefore, the research unites a set of studies and intends to respond through data mining to identify the status of the I10-I15 factors [9] with the search for the context variables associated with arterial hypertension, applying for this clustering models and association of attributes in the databases of the health sector in the city of Baghdad [10][11][12][13]. It is expected that by relating the methodology and algorithms for recognizing these patterns in HTN disease through the WEKA tool, the correlation curve of the state of said condition to factors correlated with the context variables is shown, illustrating the situation of patients between 50 and 64 years of age in the city of Baghdad, and establish good practices to stabilize these causes of death.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Risk Takers in Arterial Hypertension Patients with Data Mining Application

Alhazmi

Alassery

2022

Wireless Communications and Mobile Computing

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Hyper arterial pressure (HAP) is a disease that kills silently because it does not produce symptoms in the early stages, making it difficult to diagnose. When it is detected, its treatment is not accessible to everyone, which affects the disease’s long-term development. Hypertension affects a large portion of the Iraqi population. In the current research paper, we have discussed how data mining can be applied to identify the status of the risk factors that affect arterial hypertension due to I10-I15 causes, evaluating the context variables disability, overwork, high-risk pregnancy, stress, high diets, and poor nutrition in the population between 50 and 64 years in the city of Baghdad. It is possible to see how data mining in large volumes of health data can generate new knowledge and thus uncover hidden patterns in the data through the development of this research. Attributes directly linked to disease prevalence can be found in data from Baghdad, Iraq, even if they are not directly linked to a specific cause. This shows that some variables are transversal to the development of the disease regardless of its categorization. Cluster analysis revealed that, even though these diseases are categorized as having different causes, they have a degree of incorrect classification of 40.71% because they present attributes with a similar behavior transversal to the disease and not the disease-specific cause for which it is categorized.

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Section: Introductionmentioning

confidence: 99%

Prediction of Risk Takers in Arterial Hypertension Patients with Data Mining Application

Alhazmi

Alassery

2022

Wireless Communications and Mobile Computing

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“…In [3], a comparative analysis has been done on the Dialysis dataset using two popular tools 'Python vs Weka'. The key purpose was to choose which tool performs well by using machine learning algorithms.…”

Section: Literature Survey Of Related Workmentioning

confidence: 99%

Efficiency comparison of Python and RapidMiner

Kovács

Ghous

2020

MDT

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Machine learning is an important technique that helps companies, institutes, and humans to improve the quality of decision making. As there are many different free tools on the Internet for machine learning, we need comparisons, benchmarks to provide help in the selection of the appropriate analysis technique. This paper aims at providing a comparative study of the two most powerful and opensource machine learning tools Python and RapidMiner by using most common supervised machine learning techniques Decision Tree, Support Vector Machines, and Neural Networks on data analytic of PIMA Indian Diabetes Dataset and Mushroom Classification Dataset. The results reveal that the usage of RapidMiner provides better performance in terms of both accuracy and execution time.

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“…This section focused on the data mining experience using Python and Weka as tools that are widely used in field of data analytics [24]. The experiments were performed using a tool named Weka 3.8.3.…”

Section: Prelimanary Experimentsmentioning

confidence: 99%

Neuro-physiological porn addiction detection using machine learning approach

Kamaruddin

Wahab

Rozaidi

2019

IJEECS

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<span>Pornography is a portrayal of sexual subject contents for the exclusive purpose of sexual arousal that can lead to addiction. The availability and easy accessibility of the Internet connectivity have created unprecedented opportunities for sexual education, learning, and growth for adolescences to be in the rise. Hence, the risk of porn addiction developed by teenagers has also increased due to highly prevalent porn consumption. To date, the only available means of detecting porn addiction is through questionnaire. However, while answering the questions, participants may suppress or exaggerate their answers because porn addiction is considered taboo in the community. Hence, the purpose of this project is to develop an engine with multiple classifiers to recognize porn addiction using electroencephalography (EEG) signals and to compare classifiers performance. In the experimental study, the neuro-physiological signals of EEG data were collected previously in Indonesia among students age 9 to 13 years old by researchers from the International Islamic University Malaysia (IIUM). The EEG data were pre-processed, and relevant features are extracted using Mel-Frequency Cepstral Coefficients (MFCC). Then, the features are classified to produce the outputs of valance and arousal. Subsequently, three different classifiers of Multilayer Perceptron (MLP), Naive Bayesian (NB), and Random Forest (RF) are employed to determine whether the participant is a porn addict or otherwise. The experimental results show that the MLP classifier yields slightly better accuracy compared to Naïve Bayes and Random Forest classifiers making the MLP classifier preferable for porn addiction recognition. Although this work is still at infancy stage, it is envisaged for the work to be expanded for comprehensive porn addiction recognition system so that early intervention and appropriate support can be given for the teenagers with pornography addiction problem.</span>

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A study on using Python vs Weka on dialysis data analysis

Cited by 13 publications

References 18 publications

Prediction of Risk Takers in Arterial Hypertension Patients with Data Mining Application

Prediction of Risk Takers in Arterial Hypertension Patients with Data Mining Application

Efficiency comparison of Python and RapidMiner

Neuro-physiological porn addiction detection using machine learning approach

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