A Random Forest Students’ Performance Prediction (RFSPP) Model Based on Students’ Demographic Features

Batool, Saba; Rashid, Junaid; Nisar, Muhammad Wasif; Kim, Jung-Eun; Mahmood, Toqeer; Hussain, Amir

doi:10.1109/majicc53071.2021.9526239

Cited by 20 publications

(10 citation statements)

References 9 publications

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“…The Genetic Algorithm is used to reduce time complexity and optimize prediction results. Research proved that the proposed model is efficient with a large number of features ( 63 , 64 ). A comparative analysis is presented in ( 65 ) for the diagnosis of cardiovascular disease.…”

Section: Literature Reviewmentioning

confidence: 93%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

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Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

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Section: Literature Reviewmentioning

confidence: 93%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

Self Cite

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“…Accumulating demographic characteristics directly from the source, as opposed to self-reported responses in questionnaires, can increase the validity of the acquired data. It should be noted that many studies, including [43], have relied exclusively on demographic attributes to assess and even predict students' academic performance with high accuracy.…”

Section: Participant Demographicsmentioning

confidence: 99%

Acquiring, Analyzing and Interpreting Knowledge Data for Sustainable Engineering Education: An Experimental Study Using YouTube

et al. 2022

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With the immersion of a plethora of technological tools in the early post-COVID-19 era in university education, instructors around the world have been at the forefront of implementing hybrid learning spaces for knowledge delivery. The purpose of this experimental study is not only to divert the primary use of a YouTube channel into a tool to support asynchronous teaching; it also aims to provide feedback to instructors and suggest steps and actions to implement in their teaching modules to ensure students’ access to new knowledge while promoting their engagement and satisfaction, regardless of the learning environment, i.e., face-to-face, distance and hybrid. Learners’ viewing habits were analyzed in depth from the channel’s 37 instructional videos, all of which were related to the completion of a computer-aided mechanical design course. By analyzing and interpreting data directly from YouTube channel reports, six variables were identified and tested to quantify the lack of statistically significant changes in learners’ viewing habits. Two time periods were specifically studied: 2020–2021, when instruction was delivered exclusively via distance education, and 2021–2022, in a hybrid learning mode. The results of both parametric and non-parametric statistical tests showed that “Number of views” and “Number of unique viewers” are the two variables that behave the same regardless of the two time periods studied, demonstrating the relevance of the proposed concept for asynchronous instructional support regardless of the learning environment. Finally, a forthcoming instructor’s manual for learning CAD has been developed, integrating the proposed methodology into a sustainable academic educational process.

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“…Metode yang akan digunakan pada penelitian ini adalah metode Random Forest. Random Forest merupakan metode hasil pengembangan dari algoritma Classification And Regression Tree (CART) yang pada penerapannya menggunakan metode bootstrap aggregating (bagging) dan random feature selection [6]. Pada penelitian-penelitian sebelumnya, metode Random Forest telah banyak digunakan untuk memprediksi kinerja akademik peseta didik dalam dunia pendidikan [7] [8].…”

Section: Jiko (Jurnal Informatika Dan Komputer)unclassified

Optimasi Correlation-Based Feature Selection Untuk Perbaikan Akurasi Random Forest Classifier Dalam Prediksi Performa Akademik Mahasiswa

Priantama¹,

Siswa²

2022

JIKO

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Kegiatan pembelajaran sejak pandemi covid-19 melanda Indonesia mengharuskan institusi pendidikan melaksakanan kegiatan pembelajaran secara online atau daring. Learning Management System (LMS) menjadi salah satu solusi untuk mendukung proses pembelajaran daring. Universitas Muhammadiyah Kalimantan Timur (UMKT) memanfaatkan plat-form LMS OpenLearning sebagai upayanya untuk menjaga agar kegiatan pembelajaran tetap berlangsung dimasa pan-demi. Tujuan penelitian ini adalah untuk mengidentifikasi indikator atau atribut yang berpengaruh menggunakan metode correlation-based feature selection dan mengevaluasi kinerja algoritma Random Forest Classifier untuk memprediksi per-forma akademik mahasiswa UMKT dalam pembelajaran daring berbasis LMS OpenLearning. Pada penelitian ini, data diperoleh dari bagian administrasi akademik dan LMS OpenLearning sebanyak 2.663 data. Hasil penelitian menunjukan identifikasi korelasi atribut terbaik menggunakan correlationbased feature selection (CFS) adalah pada atribut time spent on course, course completed, tugas, uts, dan quiz. Hasil pemodelan Random Forest Classifier menggunakan optimasi CFS terbukti dapat memperbaiki akurasi pemodelan sebesar 97,22%, sedangkan pemodelan tanpa menggunakan optimasi CFS menghasilkan akurasi sebesar 91,66%.

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A Random Forest Students’ Performance Prediction (RFSPP) Model Based on Students’ Demographic Features

Cited by 20 publications

References 9 publications

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Acquiring, Analyzing and Interpreting Knowledge Data for Sustainable Engineering Education: An Experimental Study Using YouTube

Optimasi Correlation-Based Feature Selection Untuk Perbaikan Akurasi Random Forest Classifier Dalam Prediksi Performa Akademik Mahasiswa

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