Prediction of Stroke Using Deep Learning Model

Chantamit-o-pas, Pattanapong; Goyal, Madhu

doi:10.1007/978-3-319-70139-4_78

Cited by 25 publications

(9 citation statements)

References 15 publications

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“…Prediksi stroke pada pasien bertujuan untuk mengurangi potensi kematian yang disebabkan stroke. Model prediksi dengan, pembelajaran mesin telah diusulkan, antara lain menggunakan Chi-Square (Chi-2), Decision Tree [3], Two-Class Boosted Decision Tree [4], Naive Bayes, Support Vector Machine [5], Logistic Regression, Random Forest, Gradient Boosting [6]. Metoda yang diusulkan pada referensi tersebut diuji dengan menggunakan dataset yang berbeda-beda dan menghasilkan nilai akurasi yang bervariasi.…”

Section: Pendahuluanunclassified

Deep Neural Network untuk Prediksi Stroke

Faisal

Subekti

2021

JEPIN

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Pada Tahun 2019 Organisasi Kesehatan Dunia (WHO) mendudukkan stroke sebagai tujuh dari sepuluh penyebab utama kematian. Kementerian Kesehatan menggolongkan stroke sebagai penyakit katastropik karena dampaknya luas secara ekonomi dan sosial. Oleh karena itu, diperlukan peran dari teknologi informasi untuk memprediksi stroke guna pencegahan dan perawatan dini. Analisis data yang memiliki kelas tidak seimbang mengakibatkan ketidakakuratan dalam memprediksi stroke. Penelitian ini membandingkan tiga teknik oversampling untuk mendapatkan model prediksi yang lebih baik. Data kelas yang sudah diseimbangkan diuji menggunakan tiga model Arsitektur Deep Neural Network (DNN) dengan melakukan optimasi pada beberapa parameter yaitu optimizer, learning rate dan epoch. Hasil paling baik didapatkan teknik oversampling SMOTETomek dan Arsitektur DNN dengan lima hidden layer, optimasi Adam, learning rate 0.001 dan jumlah epoch 500. Skor akurasi, presisi, recall, dan f1-score masing-masing mendapatkan 0.96, 0.9614, 0.9608 dan 0.9611.

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Section: Pendahuluanunclassified

Deep Neural Network untuk Prediksi Stroke

Faisal

Subekti

2021

JEPIN

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“…The study reported the significance of the phenotypic form of classifying stroke and it explains the absence of trustworthiness in results. [16] introduced a stroke prediction model by the use of deep learning. The facts related to the issues in healthcare sector cannot be identified properly using the conventional prediction techniques.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Brain Stroke Image Classification Model Based on Artificial Bee Colony Optimization with Kernel Support Vector Machine

Manikandan*¹,

Dhanalakshmi²,

Thiruvengatanadhan³

2019

IJITEE

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In present decade, identification of abnormalities in brain gains significant attention for medical diagnosis. Though numerous existing models are available, only a few methods have been proposed which classifies a set of different kinds of brain defects. This paper introduces an efficient hybridization model for classifying the provided MR brain image as normal or abnormal. The presented model initially makes use of digital wavelet transform (DWT) for extracting features and utilizes principal component analysis (PCA) for feature space reduction. Next, a kernel support vector machine (KSVM) with radial basis function (RBF) kernel is built by artificial bee colony (ABC) for optimizing the parameters namely C and σ. For experimentation, 5-fold cross validation procedure is involved and a detailed investigation of the results takes place by comparing it with the existing models. To select the parameters, ABC algorithm has undergone a comparison with the random selection approach. The presented model is tested using a benchmark MR brain dataset. The experimental values indicated that the ABC is highly efficient for constructing optimal KSVM.

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“…The model achieved accuracy, sensitivity and specificity of 86.5%, 82.9% and 89.7%, respectively. Goyal et al [15] used a heart disease dataset for the predictive analysis of stroke by deep learning technology. Sun et al presented a method for treating functional electrical stimulation (FES) by using a finite state machine (FSM) which has been shown to be effective using additive data by accelerometer according to the calculated gain.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Al-Mekhlafi¹,

Senan²,

Rassem³

et al. 2022

Computers, Materials &Amp; Continua

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Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.

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Prediction of Stroke Using Deep Learning Model

Cited by 25 publications

References 15 publications

Deep Neural Network untuk Prediksi Stroke

Deep Neural Network untuk Prediksi Stroke

An Efficient Brain Stroke Image Classification Model Based on Artificial Bee Colony Optimization with Kernel Support Vector Machine

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

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