A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

Nahar, Nazmun; Ara, Ferdous; Neloy, Md. Arif Istiek; Barua, Vicky; Hossain, Mohammad Shahadat; Andersson, Karl

doi:10.1109/iciet48527.2019.9290507

Cited by 39 publications

(17 citation statements)

References 22 publications

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“…The data of our Parkinson disease voice dataset is limited. In future we will collect more data to detect Parkinson disease and we will work with some deep learning method and other method [2][3][4]7,8,14,17,18,20,27,28,33,38]. In future, we will also work with Parkinson disease MRI Data.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Nahar

Ara

Neloy

et al. 2021

Lecture Notes in Computer Science

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Parkinson's disease (PD) is a kind of neurodegenerative disorder characterized by the loss of dopamine-producing cells in the brain. The disruption of brain cells that create dopamine, a chemical that allows brain cells to connect with one another, causes Parkinson's disease. Control, adaptability, and rapidity of movement are all controlled by dopamine-producing cells in the brain. Researchers have been investigating for techniques to identify non-motor symptoms that show early in the disease as soon as possible, slowing the disease's progression. A machine learning-based detection of Parkinson's disease is proposed in this research. Feature selection and classification techniques are used in the proposed detection technique. Boruta, Recursive Feature Elimination (RFE) and Random Forest (RF) Classifier have been used for the feature selection process. Four classification algorithms are considered to detect Parkinson disease which are gradient boosting, extreme gradient boosting, bagging and Extra Tree Classifier. Bagging with recursive feature elimination was found to outperform the other methods. The lowest number of voice characteristics for the diagnosis in Parkinson attained 82.35% accuracy.

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Section: Discussionmentioning

confidence: 99%

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Nahar

Ara

Neloy

et al. 2021

Lecture Notes in Computer Science

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“…Nevertheless, in this analysis, only 1100 training set data were used. We will collect more data for the training process in the future and we also try to build more deep learning model [27][28][29][30][31][32] and use some traditional machine learning model [33][34][35][36][37][38][39]. As our proposed deep learning does not give the better accuracy so in future we will apply some data preprocessing technique to improve our accuracy also to handle ambient conditions.…”

Section: Discussionmentioning

confidence: 99%

Human Age Estimation Using Deep Learning from Gait Data

Pathan

Uddin

Nahar

et al. 2021

Applied Intelligence and Informatics

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Identifying people's ages and events by the use of gait information is a popular issue in our daily applications. The most popular application is health, security, entertainment and charging. A variety of algorithms for data mining and deep learning have been proposed. Many different technologies may be used to keep track of people's ages and behaviors. Existing approaches and technologies are limited by their performance, as well as their privacy and deployment costs. For example CCTV or Kinect sensor technology constitutes a privacy offense and most people do not want to make pictures or videos when they are working every day. The inertial sensor-based gait data collection is a recent addition to the gait analysis field. We have identified the age of people in this paper from an inertial sensor-data. We obtained the gait data from the University of Osaka. Convolution Neural Network (CNN) and LSTM-Based Convolution Neural Network (LSTM-CNN) are two deep learning algorithms that have been used to predict people's ages. The accuracy of age prediction via CNN is around 71.45%, while it is around 65.53% via CNN-LSTM, according to the experimental results.

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“…In future, a dataset using an accelerometer sensor will be collected elderly people with neurodegenerative disease. A deep Artificial Neural Network can be implemented in advance of achieving better performance [7], [2], [3], [19], [1][13]. More data can be used to train the learning model.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Based Fall Detection for Elderly People with Neurodegenerative Disorders

2020

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Fall is one of the most serious clinical problems faced by the elderly people. Elder people with neurodegenerative disorders like Parkinson disease often fall. This leads to the damage of physical condition and also mental condition. Therefore, elderly people should be taken care of all the time. However, it is not possible to take care of them every moment. Therefore, an automatic fall detection system is required to track elderly at any time. An automated fall detection system will provide timely assistance and hence, it will reduce medical care costs significantly. The recent developments in motion-sensor technologies have allowed the e cient use of wearable sensors in the overall treatment of the elderly. The paper presents a machine learning framework consisting of data collection, preprocessing of data, feature extraction and machine learning classifiers. They comprise C4.5, Random Forest, RepTree, and LMT (Logistic Model Tree). Dataset used in this research has been collected by using 3-axis accelerometer sensors which are mounted on a person's waist. Features have been extracted from this dataset which are used by these classifiers. C4.5 gives the highest accuracy which is 97.36% in comparison to other classifiers.

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A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

Cited by 39 publications

References 22 publications

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Human Age Estimation Using Deep Learning from Gait Data

A Machine Learning Based Fall Detection for Elderly People with Neurodegenerative Disorders

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