Depth insight for data scientist with RapidMiner « an innovative tool for AI and big data towards medical applications»

Bjaoui, Mohamed; Sakly, Houneida; Saïd, Mourad Ben; Kraïem, Naoufel; Bouhlel, Med Salim

doi:10.1145/3423603.3424059

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Four ML models: decision trees, random forest, gradient boosted trees (GBT), and support vector machine (SVM), were used in the study in order to assess the best predictive models for each of the sixteen personality constructs for the purpose of selecting a single best overall predictive model. The prediction algorithms were selected based on their suitability for the nature of the data and their suitability for answering the research questions (Bjaoui et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Hocherman

Mizrachi

Ben‐Gal

2022

Preprint

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Digital Phenotyping (DP) entails exploring digital expressions of human personality using behavioral cues drawn from smartphones’ digital footprints. Most personality-oriented DP studies focus narrowly on the Big5 model. This research aims to broaden this approach, using fifty-four personality constructs rooted in fifteen leading personality theories beyond the Big5. Our sample consisted of 104 respondents from whom smartphone data was collected over 7–10 days. We implemented both deductive (hypothesis-testing) and inductive (machine learning) modelling methods. Results show that fifteen of the sixteen broad personality constructs were successfully predicted from smartphone data (forty-eight sub-personality items of the fifty-nine types and personality traits). The best overall predictive model was Gradient Boosted Trees with communication-related features having the highest predictive weight. DP has the potential to transform the field of personality research and may be applied in areas such as HR analytics, personality-based targeted marketing, individualized homeland security, financial risk assessments, personalized medicine, and more.

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Section: Resultsmentioning

confidence: 99%

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Hocherman

Mizrachi

Ben‐Gal

2022

Preprint

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“…With a focus on providing a quick and straightforward modeling approach that is suitable for inexperienced data scientists, this research utilized RapidMiner as the platform for conducting the machine learning evaluations [22]. Figure 1 illustrates the overall framework for implementing machine learning, which includes both the auto model and manual process, along with a comparison of the results.…”

Section: Methodology 21 the Framework Of The Machine Learning Impleme...mentioning

confidence: 99%

“…The objective is to identify optimal hyperparameters and experimental settings through auto model preliminary analysis and enhance the methodology with a manual setting of machine learning based on the findings of the auto model. The approach employs in this research is simple yet powerful machine learning and allows for rapid modeling making it wellsuited for the complexities of reverse migration [21,22]. By filling a gap in the social science research domain, where there is a shortage of studies on this topic and a lack of expert data scientists, our methodology provides an accessible and efficient means of generating insights into this important issue.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Reverse Migration Classification

Nur Huzeima Mohd Hussain,

Azreen Anuar,

Suraya Masrom

et al. 2024

ARASET

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Reverse migration has become a more pressing issue in recent times, owing to a range of factors like economic downturns, political instability, natural disasters, and the COVID-19 pandemic. The pandemic, in particular, has highlighted the vulnerability of migrant workers in urban areas, leading many to return to their rural homes. As a result, reverse migration necessitates focused attention and planning by governments, policymakers, and communities to ensure favourable outcomes for all parties involved. This paper aims to provide a fundamental research framework from research that utilized a machine learning approach to classify reverse migration based on evidence from Selangor, Malaysia. The research methodology involves selecting features for reverse migration classification models and identifying optimal hyperparameters and experimental settings through auto model preliminary analysis. Furthermore, based on the findings of the auto model, the methodology was enhanced with a manual setting of machine learning. Three machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosted Trees were used. The results of the auto model and the manual process that used different split ratios were compared. All the machine learning algorithms performed with a high accuracy of over 90% and were efficient in completing prediction tasks in under a minute across various settings. The best machine learning model with an accuracy of 97.6% is Gradient Boosted Trees with a split ratio of 60:40. The paper presents findings that could prove useful for governments, legal planners, investors, and the community in strategizing and surviving through an artificial intelligence prediction approach.

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“…It has the Visual Workflow Designer tools to create ML workflows, and each step is documented for complete transparency. This part of the tool allows connecting the data source, automated in-database processing, data visualization as well as the Model Validation process (Bjaoui et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

KoopaML, a Machine Learning platform for medical data analysis

García‐Holgado

Vázquez-Ingelmo²,

Alonso-Sánchez³

et al. 2022

JIS

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Machine Learning allows facing complex tasks related to data analysis with big datasets. This Artificial Intelligence branch allows not technical contexts to get benefits related to data processing and analysis. In particular, in medicine, medical professionals are increasingly interested in Machine Learning to identify patterns in clinical cases and make predictions regarding health issues. However, many do not have the necessary programming or technological skills to perform these tasks. Many different tools focus on developing Machine Learning pipelines, from libraries for developers and data scientists to visual tools for experts or platforms to learn. However, we have identified some requirements in the medical context that raise the need to create a customized platform adapted to end-user found in this context. This work describes the design process and the first version of KoopaML, an ML platform to bridge the data science gaps of physicians while automatizing Machine Learning pipelines. The platform is focused on enhanced interactivity to improve the engagement of physicians while still providing all the benefits derived from the introduction of Machine Learning pipelines in medical departments, as well as integrated ongoing training during the use of the tool’s features.

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Depth insight for data scientist with RapidMiner « an innovative tool for AI and big data towards medical applications»

Cited by 11 publications

References 14 publications

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Machine Learning in Reverse Migration Classification

KoopaML, a Machine Learning platform for medical data analysis

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