Stroke Prediction Using Machine Learning in a Distributed Environment

Rajora, Maihul; Rathod, Mansi; Naik, Nenavath Srinivas

doi:10.1007/978-3-030-65621-8_15

Cited by 18 publications

(10 citation statements)

References 8 publications

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“…In particular, the RF approach, an advanced implementation of decision trees, has outperformed many powerful learning techniques which have proven themselves to be popular in other fields. 41 Each tree in the ensemble is constructed from a sample drawn with a replacement from the training set in RFs. Besides, during the construction of a tree, when splitting each node, the best split is found either from all input features or from a random size subset.…”

Section: Methodsmentioning

confidence: 99%

CNN-based severity prediction of neurodegenerative diseases using gait data

2022

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Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.

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Section: Methodsmentioning

confidence: 99%

CNN-based severity prediction of neurodegenerative diseases using gait data

2022

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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

“…Hal ini menjadi kurang tepat untuk membandingkan antar hasil dari berbagai algoritma machine learning tersebut. Hasil dari pengujian secara distributed environment didapatkan nilai tertinggi menggunakan algoritma Gradient Boosting dengan akurasi sebesar 94,49% [6].…”

Section: Pendahuluanunclassified

“…Salah satu metode yang prospek untuk menghasilkan akurasi yang lebih tinggi dibandingkan dengan algoritma Gradient Boosting [9] Makalah ini disusun sebagai berikut: Bagian II menjelaskan metode yang kami usulkan. Bagian ini membahas explorasi dataset yang digunakan sampai dengan arsitektur model DNN.…”

Section: Pendahuluanunclassified

“…Penelitian menggunakan dataset stroke prediction yang diunduh dari [10]. Penggunaan data tersebut agar dapat diperbandingkan dengan metode yang sudah dilakukan oleh penelitian sebelumnya, antara lain [6]. Disamping itu, penelitian dilakukan untuk mengatasi kelemahan metode yang dilakukan peneliti sebelumnya.…”

Section: A Dataset Descriptionunclassified

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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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A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques

Kumar

Kumar²

2022

Communications in Computer and Information Science

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Stroke Prediction Using Machine Learning in a Distributed Environment

Cited by 18 publications

References 8 publications

CNN-based severity prediction of neurodegenerative diseases using gait data

CNN-based severity prediction of neurodegenerative diseases using gait data

Deep Neural Network untuk Prediksi Stroke

A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques

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