Classification of ALS disease using support vector machines

Kucuk, Hanife; Eminoğlu, İlyas

doi:10.1109/siu.2015.7130171

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Pattekari et al [33] created a web-based tool that uses Naive Bayes to predict myocardial infarction. Küçük et al [34] used the SVM model in a high-dimensional medical dataset in order to correctly detect cases of diabetes. Saravana Kumar et al [35] developed a system to analyze diabetic patient information using Hadoop and the Map Reduce approaches.…”

Section: Related Workmentioning

confidence: 99%

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Alqushaibi¹,

Hasan²,

Abdulkadir³

et al. 2023

Computers, Materials &Amp; Continua

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Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world's diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals' lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.

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Section: Related Workmentioning

confidence: 99%

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Alqushaibi¹,

Hasan²,

Abdulkadir³

et al. 2023

Computers, Materials &Amp; Continua

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“…Data ini menunjukan, data yang muncul paling dekat dengan hyper-plane, dikenal sebagai Support Vectors [15]. [15] Kinerja yang dievaluasi dari algoritma SVM untuk prediksi diabetes [16], [17]…”

Section: Deskripsi Singkat Algoritma Yang Digunakanunclassified

Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest

Apriliah¹,

Kurniawan

Baydhowi³

et al. 2021

SISTEMASI

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Diabetes adalah salah satu penyakit kronis yang mengancam jiwa dengan pertumbuhan tercepat yang telah mempengaruhi 422 juta orang di seluruh dunia menurut laporan Organisasi Kesehatan Dunia (WHO), pada tahun 2018. Diabetes dianggap sebagai salah satu penyakit paling mematikan dan kronis yang menyebabkan peningkatan gula darah. Banyak komplikasi terjadi jika diabetes tetap tidak diobati dan tidak teridentifikasi. Namun, peningkatan pendekatan machine learning memecahkan masalah kritis ini. Tujuan dari penelitian ini adalah merancang model yang dapat memprakirakan kemungkinan terjadinya diabetes pada pasien dengan ketelitian yang maksimal. Klasifikasi adalah teknik data mining yang menetapkan kategori pada kumpulan data untuk membantu dalam memprediksi dan analisis yang lebih akurat. Oleh karena itu tiga algoritma klasifikasi machine learning yaitu Suport Vector Machine, Naive Bayes dan Random Forest digunakan dalam percobaan ini untuk mendeteksi diabetes secara dini. Eksperimen dilakukan menggunakan dataset Diabetes Hospital in Sylhet, Bangladesh yang bersumber dari UCI repository. Performa ketiga algoritma dievaluasi pada berbagai ukuran seperti Precision, Accuracy, F-Measure, dan Recall. Akurasi diukur melalui instance yang diklasifikasikan dengan benar dan salah. Hasil yang diperoleh menunjukkan Random Forest mengungguli dengan nilai akurasi tertinggi 97,88% dibandingkan algoritma lain. Hasil ini diverifikasi menggunakan kurva Receiver Operating Characteristic (ROC) secara tepat dan sistematis.

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“…Numerous classifiers applied for diagnosis of muscular disorders [13,14]. Time and frequency based features with support vector machine used for classification of EMG signals Health Information Science and Systems Sengur et al Health Inf Sci Syst (2017) 5:9 [15]. Dimension reduction methods have compared for classification of EMG signals [16].…”

Section: Introductionmentioning

confidence: 99%

Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm

Şengür

Akbulut

Guo

et al. 2017

Health Inf Sci Syst

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Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.

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Classification of ALS disease using support vector machines

Cited by 5 publications

References 5 publications

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest

Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm

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