AdaBoost for Parkinson's Disease Detection using Robust Scaler and SFS from Acoustic Features

Reddy, Kakunuri Venkata Ashok; Ambati, Srujith Rao; Reddy, Yennam Shiva Rithik; Reddy, Agumamidi Nithish

doi:10.1109/stcr51658.2021.9588906

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…The data after dimensionality reduction were subsequently used as input for the ensemble learning algorithm, facilitating the training process, and ultimately leading to the construction of the final predictive model for anticancer peptides. It is worth mentioning that the RobustScaler ( Reddy et al, 2021 ) method is a robust approach for scaling numerical features, providing reliable and accurate results across various machine learning tasks. Additionally, the PCA algorithm was also employed to reduce dimensionality while preserving valuable information in the feature vectors.…”

Section: Methodsmentioning

confidence: 99%

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Liu,

Wu,

et al. 2024

Front. Genet.

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Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments.Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/.Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

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Section: Methodsmentioning

confidence: 99%

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Liu,

Wu,

et al. 2024

Front. Genet.

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“…Robust scaler merupakan salah satu metode feature scaling yang digunakan dalam analisis data. Metode ini bertujuan untuk mengubah skala fitur-fitur dalam dataset agar lebih tahan terhadap outlier atau nilai ekstrem [11] . Outlier dapat mempengaruhi perhitungan statistik seperti rata-rata dan simpangan baku, dan dapat memiliki dampak yang signifikan pada hasil analisis data [12] .…”

Section: Pendahuluanunclassified

“…Hasil Eksperimen memperlihatkan bahwa model prediksi klasifikasi algoritma K-Nearest Neighbors menggunakan metode scaling robust scaler memperlihatkan rata-rata nilai lebih baik dibandingkan dengan tanpa robust scaler dengan nilai accuracy sebesar 0.82, precision sebesar 0.87, recall sebesar 0.80 dan F1 score sebesar 0.83. Penelitian yang dilakukan [13] terhadap penyakit parkinson, penelitiannya berfokus pada standarisasi data yang diberikan dan pemilihan fitur sekuensial untuk menganalisis karakteristik yang dipilih. Penelitiannya menggunakan algoritma klasifikasi Random Forest, Logistic Regression, SVM, Gaussian Naive Bayes, dan KNN.…”

Section: Pendahuluanunclassified

Teknik Scaling Menggunakan Robust Scaler Untuk Mengatasi Outlier Data Pada Model Prediksi Serangan Jantung

Nugraha,

Sabaruddin,

Murni

2024

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Serangan jantung adalah salah satu faktor utama dalam tingginya tingkat angkat penyebab kematian di seluruh dunia dan memerlukan prosedur diagnosa yang canggih sehingga dapat mengakibatkan peningkatan biaya yang signifikan. Memprediksi penyakit jantung merupakan tantangan utama dalam bidang kesehatan karena keterbatasan peralatan diagnosis penyakit ini. Prediksi penyakit jantung yang akurat sangat penting untuk mengobati pasien sebelum serangan jantung terjadi. Prediksi ini dapat dicapai dengan menggunakan model pembelajaran mesin (machine learning) yang optimal dengan data layanan kesehatan yang kaya (datasets) mengenai penyakit jantung. Namun, Permasalahan yang umumnya dihadapi oleh model prediksi penyakit jantung seperti data yang menyimpang secara ekstrim (outliers), data yang hilang, data yang tidak konsisten, dan data yang tercampur baik secara numerik maupun kategorikal. Data yang tidak konsisten menyebabkan kemungkinan kesalahan prediksi dan akan mempengaruhi hasil dari prediksi. Pada penelitian ini kami mencoba mengatasi masalah outlier pada dataset penyakit jantung menggunakan salah satu metode feature scaling yaitu robust scaler. Hasil Eksperimen dengan model klasifikasi algoritma K-Nearest Neighbors menggunakan metode scaling robust scaler memperoleh nilai lebih baik dibandingkan dengan tanpa robust scaler dengan nilai F1 score sebesar 0.86.

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“…learning pipeline in order to enhance the performance of the ML algorithms. As a dataset usually contains various different features and each one can have a different range of values or units of measure, RS was used to scale up the dataset inputs [50]. RS transforms the feature vector: each value is subtracted from the feature median and is then divided by the interquartile range (IQR), which is the difference between the 75th percentile and 25th percentile using the formula:…”

Section: ) Data Preprocessingmentioning

confidence: 99%

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Halman

Alenazi

2023

IEEE Access

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The healthcare sector deals with sensitive and significant data that must be protected against illegitimate users. Software-defined networks (SDNs) are widely used in healthcare systems to ensure efficient resource utilization, security, optimal network control, and management. Despite such advantages, SDNs suffer from a major issue posed by a wide range of cyberattacks, due to the sensitivity of patients' data. These attacks diminish the overall network performance, and can cause a network failure that might threaten human lives. Therefore, the main goal of our work is to propose a machine learning-based cyberattack detector (MCAD) for healthcare systems, by adapting a layer three (L3) learning switch application to collect normal and abnormal traffic, and then deploy MCAD on the Ryu controller. Our findings are beneficial for enhancing the security of healthcare applications by mitigating the impact of cyberattacks. This work covers the testing of MCAD using a wide spectrum of both ML algorithms and attacks, and provides a performance comparison for every pair of ML algorithms/attacks to illustrate the strengths and weaknesses of different algorithms against a specific attack. The MCAD shows impressive performance, achieving an F1-score of 0.9998 and of 0.9882 on normal and attack classes, respectively, which implies a high level of reliability. MCAD also achieved 5,709,692 samples per second on throughput, which reflects a high-performance real-time system with respect to complexity. Additionally, it showed a positive impact on the network KPIs by increasing the throughput by 609%, and decreasing delay and jitter by 77% and 23%, respectively, compared to attack results.

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AdaBoost for Parkinson's Disease Detection using Robust Scaler and SFS from Acoustic Features

Cited by 9 publications

References 15 publications

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Teknik Scaling Menggunakan Robust Scaler Untuk Mengatasi Outlier Data Pada Model Prediksi Serangan Jantung

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

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