Analisis Metode Klasifikasi Penyakit Bell's Palsy Menggunakan Machine Learning
DOI:
https://doi.org/10.36312/ej.v5i1.1610Keywords:
Klasifikasi, Penyakit Bell’s Palsy, Machine LearningAbstract
Bell’s Palsy adalah satu kondisi yang mempengaruhi saraf wajah, yang menyebabkan kelemahan atau kelumpuhan tiba-tiba pada otot di satu sisi wajah. Klasifikasi Penyakit Bells’ Palsy sangat penting untuk diagnosis dan prognosis yang akurat. Dalam beberapa tahun terakhir, Algoritma pembelajaran mesin telah dieskplorasi sebagai alat potensial untuk mengklasifikasikan penyakit Bell’s Palsy berdasarkan jenis data, seperti informasi klinis, data pencitraan dan elektrodiagnostik. Dalam tinjauan Pustaka sistematis ini, kami menganalisis keadaan peneliti saat ini tentang penggunaan algoritma mesin learning untuk mengklasifikasikan penyakit Bell’s Palsy, dengan metode Sistematic Literatur Riview (SLR) dengan mengumpulkan hasil penting dari literatur yang dikaji. Hasil penemuan kami menunjukkan beberapa penelitian telah menggunakan berbagai jenis algoritma mesin learning seperti Support Vector Machine (SVM) dan Convolutional Neural Network (CNN). Algortima tersebut menunjukkan tingkat akurasi yang tinggi dalam mengklasifikasikan penyakit Bell’s Palsy dan memprediksi tingkat keparahan atau hasilnya. Namun penelitian lebih lanjut diperlukan untuk memvalidasi temuin ini dan mengeksplorasi potensi penggunaan jenis data lain untuk tujuan klasifikasi. Penggunaan algortima mesin learning untuk klasifikasi penyakit Bell’s palsy berpotensi meningkatkan akurasi diangnosisi dan prognosis serta meningkatkan penatalaksanaan kondisi ini secara keselruhan dengan menggunakan berbagai macam data dengan menganalisisnya secara akurat sehinga dokter dapat merencakan perawatan yang disesuaikan dengan kebutuhan unik pasien dengan memprediksi tingkat keparahan dan hasil pengobatan.
Analysis of Bell's Palsy Disease Classification Methods Using Machine Learning
Abstract
Bell's palsy is a condition that affects the facial nerves, causing sudden weakness or paralysis of the muscles on one side of the face. The classification of Bell's palsy disease is crucial for accurate diagnosis and prognosis. In recent years, machine learning algorithms have been explored as a potential tool to classify Bell’s palsy disease based on data types such as clinical information, imaging data, and electrodiagnostics. In this systematic review of the library, we analyzed the current state of research on the use of machine learning algorithms to classify Bell’s palsy disease using the Riview Systematic Literature (SLR) method by collecting important results from the literature studied. Our findings suggest that some studies have used different kinds of machine learning algorithms, such as support vector machines (SVM) and convolutional neural networks . (CNN). The algorithm shows a high degree of accuracy in classifying Bell's palsy disease and predicting its severity or outcome. But further research is needed to validate these findings and explore the potential use of other types of data for classification purposes. Using machine learning algorithms to classify Bell's palsy disease has the potential to improve the accuracy of anginosis and prognosis as well as the implementation of the condition in a comprehensive way by using a wide range of data and analyzing it accurately, so long as doctors can plan treatment that is tailored to the unique needs of patients by predicting the severity and outcome of treatment.
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