Preprocessing Handling to Enhance Detection of Type 2 Diabetes Mellitus based on Random Forest

Ramadhan, Nur Ghaniaviyanto; Adiwijaya, Adiwijaya; Romadhony, Ade

doi:10.14569/ijacsa.2021.0120726

Cited by 13 publications

(18 citation statements)

References 23 publications

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“…First, we applied the SUS score to two voice user interfaces: Amazon Alexa Echo Dot and Google Nest Mini. Although SUS has been designed to be applied to all types of systems ( Gronier & Baudet, 2021 ) such as augmented reality ( Hatzl et al, 2023 ), websites ( Igarashi, Kobayashi & Nihei, 2023 ), mobile applications ( Moorthy et al, 2023 ), expert systems ( Azmi et al, 2023 ), serious games ( Höhler et al, 2023 ) and e-learning systems ( Heinzel et al, 2020 ), in the literature there is a lack of studies that apply SUS to voice users interfaces. Therefore, we validated the System Usability Scale as a metric to evaluate voice users interfaces, an emerging field.…”

Section: Discussionmentioning

confidence: 99%

Validation of system usability scale as a usability metric to evaluate voice user interfaces

Deshmukh,

Chalmeta

2024

PeerJ Computer Science

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In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface’s usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces.

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Section: Discussionmentioning

confidence: 99%

Validation of system usability scale as a usability metric to evaluate voice user interfaces

Deshmukh,

Chalmeta

2024

PeerJ Computer Science

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“…According to N.G. Ramadhan, et al [20] in 2021, The predictions derived from laboratory data for diabetes showed an enhancement in precision and recall results by 20-24%. The study pinpointed three factors influencing diabetes prediction accuracy: (i) the quantity of missing values, (ii) the utilization of influential features, and (iii) an imbalance in the number of positive and negative cases.…”

Section: A Current Trend Of Chronic Disease Predictionmentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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“…Random forest adalah metode pembelajaran ensemble untuk klasifikasi atau regresi yg beroperasi menggunakan menciptakan banyak pohon keputusan selama proses training dan menaruh hasil berupa mode kelas (klasifikasi) atau prediksi rata-rata (regresi) pohon individu [20]. Selain itu random forest merupakan model klasifikasi supervised learning yang banyak digunakan diberbagai ilmu disiplin klasifikasi [21].…”

Section: Random Forestunclassified

Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression

Ramadhan¹,

Adhinata²,

Segara³

et al. 2022

Jur. Ris. Kom.

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Fake news is information that is presented incorrectly or falsely. Of course, if the spread of fake news continues, it can result in wrong knowledge of the information obtained. One of the efforts to prevent the spread of fake news is by detecting whether the news is genuine or fake in order to provide an explanation to the readers of the related news. This study aims to detect fake news using a supervised learning random forest model. The news dataset used contains 6256 rows of titles that have a fake or real class. The dataset first goes through a cleaning, tokenization, and stemming process to break sentences into words. The results obtained using the random forest model of 84%, this result is higher than using the logistic regression model of 77%.

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Preprocessing Handling to Enhance Detection of Type 2 Diabetes Mellitus based on Random Forest

Cited by 13 publications

References 23 publications

Validation of system usability scale as a usability metric to evaluate voice user interfaces

Validation of system usability scale as a usability metric to evaluate voice user interfaces

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression

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