Hybrid particle swarm - based fuzzy support vector machine for hypoglycemia detection

Abstract

Severe hypoglycemia is potentially life-threatening. This article introduces a novel hypoglycemia detection strategy using a hybrid particle swarm -based fuzzy support vector machine (SFisSvm) technique. The inputs of this system are six electrocardiographic (ECG) parameters. The system parameters of SFisSvm are optimized using a particle swarm optimization method. The proposed hypoglycemia detector system is a combination of two subsystems, namely, fuzzy inference system (FIS) and support vector machine (SVM). Two most significant inputs, heart rate and RTp c are fed to FIS, and its output is used for input of the SVM. The other ECG parameters and the output of FIS are fed to SVM and, then, are classified to indicate the presence of hypoglycemia. In this study, three and five membership functions are investigated for FIS. Furthermore, radial basis function (RBF), sigmoid and linear kernel functions are employed for mapping the inputs to high dimensional space in SVM. Performances of SFisSvm with different kernel functions are compared. As conclusion, the performance of SFisSvm is found with 75.19%, 83.71% and 79.33% in terms of sensitivity, specificity and geometric mean.

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

“…Regarding the BG data, various preprocessing approaches had been used including differencing (derivative) BG values [27,28], CGM data reconstruction, or smoothing using different methods such as spline interpolation [29-33], a rough feature elimination, such as fast separability and correlation analysis algorithm [28,29], representing BG temporal change information [34], feature selection and feature ranking [35], filtering using Pearson’s correlation coefficient (PCC) and the t test, and the wrapper approach using greedy backward elimination [33]. The other physiological parameters (heart rate, ECG, skin impedance, and others) had been preprocessed using different methods such as normalization [36-38], feature extraction and selection [39,40], feature extraction using fast Fourier transform (FFT) [41], unsupervised restricted Boltzmann machine–based feature representation [42], filtering techniques such as Infinite impulse response high pass filter [41,43], correlation analysis [44-46], and transformation of frequency domain into time domain (FFT) [47].…”

“…For example, Nuryani et al [39,79] proposed a hybrid fuzzy SVM and investigated the applicability of 3 KFs: radial basis, exponential radial basis, and polynomial function for the task. Moreover, Nuryani et al [40,80] also further developed a novel strategy using a hybrid particle swarm-based fuzzy SVM technique. Fuzzy reasoning models are also tested in some of the studies.…”

Diseminasi Riset Kecerdasan Buatan Untuk Diagnosis Medis Berbasis Elektrokardiogram di Universitas Duta Bangsa

“…Pada sistem ini digunakan mesin pembelajaran support vector machine (SVM). Teknik SVM menunjukkan kemampuan yang baik untuk klasifikasi dalam berbagai bidang, diantaranya pada bidang industri (Muthukrishnan, Krishnaswamy et al 2020), bidang pertanian (Das, Singh et al 2020), bidang ekonomi (Zahariev, Zveryаkov et al 2020) dan bidang-bidang lainnya, Untuk itu maka SVM juga telah diuji kinerjanya pada bidang medis pada berbagai macam gangguan penyakit, seperti paru-paru (Mahdy, Ezzat et al 2020), diabetes (Nuryani, Ling et al 2012) dan sebagainya. Pada sistem yang dipaparkan ini SVM digunakan untuk sistem deteksi fibrilasi atrium.…”

Sistem Deteksi Kelainan Neuromuscular Menggunakan Adaptive Neuro Fuzzy Inference System dan Dekomposisi Wavelet Elektromyogram

“…Hasil karakterisasi sinyal kemudian diidentifikasi menggunakan metode klasifikasi ANFIS. Sistem ini merupakan perpaduan Fuzzy Inference System (FIS) [10] yang diaplikasikasikan dalam metode pembelajaran Artificial Neural Network (NN).…”