Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_145_библиотеки_им_акад_М_И_Перельмана
.pdf
Prognosis of Dementia Disease Prediction using Machine Learning 83
https://t.me/med1917
developed using advanced characteristics paired with six separation techniques.
Among the three strategies for selecting features, Acquiring Information has
proven to be the most successful. The Naive Bayes algorithm outperformed the
other 6 split models. This study's diagnostic approach provides clinicians with a
useful tool for diagnosing dementia in its early stages.
The authors of a study tested three machine learning algorithms, the Artificial
Neural Network (ANN), vector assistance system (SVM), and adaptive neurofuzzy inference (ANFIS) device. For 60 patients (33 AD, 27 VD), many regional
metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI)
were employed as input features for training. Using their fundamental MRI data,
the discriminatory VD-AD pattern is then used to include a set of ML rules that is
extremely effective in predicting the occurrence of 15 degenerative patients with a
scientific profile of “mixed VD – AD” (MXD). The analytical evidence from a 3year scientific follow-up was compared to the ML predictions. ANFIS evolved as
a set of high-green criteria for distinguishing AD from VD, with a low-level
pattern achieving an accuracy of more than 84 % [2].
Furthermore, when trained with a true set of multimodal input features (e.g., DTI
+ rs-fMRI) substitutes for a set of unstructured labour records, ANFIS ensured
enhanced phase accuracy. ANFIS completed a projected price of 77.33 %
utilizing a sample of quality discrimination in the MXD group. Overall, the
findings revealed that our method has the potential to be exceedingly
discriminatory when it comes to distinguishing between AD and VD profiles.
The authors [3] used data from 7031 studies over the age of 65 gathered between
2001 and 2005 from the Korean National Health and Nutrition Examination
Survey (KNHANES) to develop a deep neural network (DNN) to predict
dementia using health care and medical service data. Component-specific analysis
(PCA) with small/large scale measurements is the proposed approach for
anticipating and extracting significant background elements. They compared
DNN/scaled PCA, the suggested technique, against five well-known machine
learning algorithms. The proposed method achieves an AUC of 85.5 %, which is
higher than that of existing algorithms.
The authors of the paper [4] evaluated the packaging of two models, a gradient
magnification machine and an Artificial Neural Network, both of which are used
to forecast dementia, and mathematical analysis was employed to confirm their
usefulness.
In a two-step method, the authors of the paper [5] used two Machine Learning
algorithms to forecast the disease. To begin, the ML model was trained to predict
future generic diagnoses (NC) vs defective comprehension (MCI or AD). Second,
84 Disease Prediction using Machine Learning Saini et al.
https://t.me/med1917
using fundamental features, a three-phase breakdown was performed to predict
direct the follow-up diagnosis (NC, MCI, AD).
Nine different learning mechanisms were considered for examination in the paper
[6]. Support Vector machine was the most common, with 46/76 (61%) when used
alone and 53/76 (70%) when integrated with another machine learning technique,
followed by direct discriminatory analysis (6/76, 8%), depletion (4/76, 5%), and a
few neighbours close to exploration, such as orthogonal speculation to hidden
buildings, unplanned forest, or a partial representation. The majority of analysis
(91/144, 63 %) employed only T1 pictures, with a small number employing T1
plus other sequences, other forms of data, or both. Each analyzed sample has a
population of 100 to 902. More than 300 (51/144, 35%) or fewer than 150
(45/144, 31%) were found in the same analysis.
The work presented [7] shows a comparative analysis of the learning algorithms
of four machines: the J48, the NaiveBayes, the Random Forest, and the Multilayer
Perceptron. CFS Subset Eval is used to reduce the attributes. When compared to
all dementia algorithms, J48 has proven to be quite effective.
Using data from OASIS, the authors of the paper [8] devised a machine-based
learning approach for diagnosing dementia. To determine accuracy, memory, and
the matrix of confusion, techniques such as SVM, AdaBoost, random forest,
Linear Discriminative analysis, XgBoost algorithms, and K-Nearest neighbors are
utilized. The accuracy of the following algorithms ranges from 83 to 90%. SVM
has an accuracy of 87%, KNN has an accuracy of 84%, LDA has an accuracy of
83%, the random forest has an accuracy of 88%, AdaBoost has an accuracy of
81%, and XgBoost has an accuracy of 90%. XGBoost outperforms other
algorithms in terms of accuracy.
In a paper [9], authors achieved an average accuracy of about 92% using binary
classification (dementia vs. non-dementia), while the full range of multistage
prediction attained 77% accuracy using regression, followed by a random jungle
of 92% and 70% respectively. The findings reveal that a learning machine can
anticipate psychological damage based on changeable risk variables, which could
lead to actions to prevent the disorder from spreading or becoming more severe in
many people.
In the paper [10], the authors used hyper-parametric classifiers from Nave Bayes
Models, Support vector machine (SVM), Decision Trees, XGBoost, Gradient
Boost, Ensemble, Adaboost, Random forest models, and LGBM (Light gradient
boosting framework machine). The model was created in Python using MCapNet,
and the results were verified by comparing them to other cutting-edge machine-
Prognosis of Dementia Disease Prediction using Machine Learning 85
https://t.me/med1917
learning methods. The proposed technique had the highest accuracy when
compared to other strategies, with a score of 92.39%.
In recent years, machine learning-based approaches such as supervised machine
learning have become more widely applied in the field of healthcare. Heart
disease, diabetes, lung cancer, breast cancer, and other diseases have been
successfully detected using computer-assistive detection systems that use
learning-based methodologies. Both clinicians and patients can benefit from this
information if it is discovered early enough.
METHODOLOGY
The goal is to develop and validate an effective approach using Machine Learning
to assist in the initial diagnosis of dementia.
To develop and evaluate the proposed system, following steps are used:
1. Data collection: In this step, we have collected data on dementia prediction.
2. Clean and Analyse Data: filtered the necessary (useful) data so that our model
could predict dementia efficiently and effectively.
3. Training Algorithm: An appropriate training algorithm, algorithm we have used
to predict dementia is CNN & RFA.
4. Evaluate: The test of our model falls into this category.
Proposed Model for Predicting Dementia using Patient Record and MRI
Fig. (1) depicts the proposed model for predicting dementia using patient records.
The following is the flow chart of this model’s working. Also, the Deep Learning
based model proposed for predicting dementia is shown in Fig. (2).
In the first step, the model creates new datasets from the given data, a technique
known as bootstrapping. In the second step, it creates multiple decision trees
based on the new datasets, the results of which will be our predictions, and the
result will be the average of those predictions, a technique known as
aggregation.When aggregation and bootstrap both occur, the technique is known
as bagging.
86 Disease Prediction using Machine Learning Saini et al.
https://t.me/med1917
TRAINING DATA
BOOTSTRAPED
DATASETS
n trees
Aggregation
Fig. (1). Proposed model for predicting dementia using patient record.
Creating new
dataset 1
Prediction 1 Prediction 2 Prediction n
Input
Creating new
dataset 2
RESULT: AVERAGE OF
TRESS PREDICTION
Pooling
layer
Creating new
dataset n
output
Fig. (2). Proposed model for predicting dementia using MRI.
Convolution
layer
RELU layer
Demented
Non-
Demented
Fully connected
layer
Prognosis of Dementia Disease Prediction using Machine Learning 87
https://t.me/med1917
RESULT ANALYSIS
Fig. (3) depicts the CNN model's output; we used random demented MRI from
our test dataset to verify the model's functionality. As can be seen in Fig. (3), the
proposed model accurately predicted the sickness.
Fig. (3). Result matching with dementia.
Further, we employed random non-demented (healthy) MRIs from our test dataset
to confirm that our model was performing properly, and as shown in Fig. (4), this
model predicted the result accurately and with high accuracy.
Fig. (4). Result matching with no dementia (Healthy).
88 Disease Prediction using Machine Learning Saini et al.
https://t.me/med1917
The result of the RFA model is displayed in Fig. (5). We used random
nondemented (healthy) patient data from our test dataset to see if our model was
working properly or not, and as shown in Fig. (5), this model was predicted
correctly and with remarkable accuracy.
Sex (0-male, female-1)
0
Education
6
Age at Ist TBI
0
Nia Reagan
0
Cerad Score
0
Num TBI Loc
0
56
1
2
Fig. (5). Dementia-free results.
Sex (0-male, female-1)
0
Education
9
6
Age at Ist TBI
0
Nia Reagan
0
Cerad Score
0
Num TBI Loc
0
56
1
1
21
89
3
3
2
3
3
3
19
1
1
21
89
3
3
2
1
TBILoc
0
0123456789
Braak
Your Report:
You are healthy (You do not have
3
1
3
any Dementia)
Accuracy:
93.18181818181817%
2
1
TBILoc
0
0123 546789
Your Report:
Braak
Your symptoms matches with
Dementia, Please consult to a
Doctor
Accuracy:
88.63636363636363%
Fig. (6). Dementia-related results.
The result of the RFA model is presented in Fig. (6). We used random demented
patient data from our test dataset to confirm our model was working properly, and
as shown in Fig. (6), this model was predicting disease correctly and with
remarkable accuracy. Using RFA and CNN, this model achieved more than 92 %
accuracy in predicting dementia from a patient's narrative or magnetic resonance
imaging (MRI). This work is implemented on localhost.
Prognosis of Dementia Disease Prediction using Machine Learning 89
https://t.me/med1917
The graph shown in Fig. (7), and results shown in Table 1 demonstrate the
performance of seven Machine Learning models that have been implemented.
Implemented model used to predict Dementia (Accuracy Vs Model)
120.00%
100.00%
ACCURACY %
80.00%
60.00%
40.00%
20.00%
0.00%
80.76%
K-Nearest
Neighbours
88.29%
53.87%
Random Forest K-Nearest
Neighbours
93.22%
Random Forest
MODEL USED
99.99%
Deep Convolutional
Neural Network
90.12%
66.70%
XGBoost SVM
Fig. (7). Comparison in performance of machine learning models.
Table 1. Learning-based Alzheimer's disease early detection strategies.
Dataset Model Accuracy
Oasis K- Nearest Neighbours 80.7641%
ADNI Random Forest 88.2901%
ADNI
K- Nearest Neighbours
Kaggle* Random Forest
Kaggle* Deep Convolutional Neural Network 99.99%
ADNI XGBoost 90.121%
Kaggle Support Vector Machine 66.70%
Note: The rows with * are the algorithms being implemented to predict dementia in this work.
53.87%
93.219%
CONCLUSION
In this study, we developed a Random Forest randomized controlled trial that can
effectively distinguish between Dementia patients and healthy people based on
their reports. The model's capacity to predict patients in the early stages of
Alzheimer's disease was based on the use of the random forest algorithm and
CNN to choose appropriate training variables. The model's effectiveness was
90 Disease Prediction using Machine Learning Saini et al.
https://t.me/med1917
evaluated and contrasted utilising a variety of mathematical analysts. It was
considered the most suitable model to distinguish between the supplied variance
(MCI patients and HCS) with high accuracy, sensitivity, and clarity based on the
random forest model of operation. The significance of the precision found in the
random forest with various sizes of independent experimental data was compared
to other models and a significant increase was observed when compared to the
model chart analysis in independent experimental data showing that the random
forest model could improve prediction three times over. As a result, we utilized an
ML-based differentiation model based on early CSF biomarkers. The current
work is a step forward in the prognosis of Alzheimer's disease in its initial stages.
In the future, we plan to develop a web-based guessing system to test on people in
the early stages of dementia, using biomarker features from the Alzheimer's
preclinical database.
ACKNOWLEDGMENTS
We would like to express our gratitude towards our mentor, Dr. Anu Saini for
exposing us to this topic, providing us with research material, and for being the
guiding light all this while. Furthermore, we would like to thank Ms. Sunita
Kumari for helping us in writing this paper and our principal, for presenting us
with this golden opportunity of making this project. Finally, we would like to
thank our friends and family for being the constant support system and
encouraging force all this while.
REFERENCES
[1] M.J. Prince, A. Wimo, M.M. Guerchet, G.C. Ali, Y.T. Wu, and M. Prina, World Alzheimer Report
2015 The Global Impact of Dementia An Analysis of Prevalence, Incidence, cost and trends.
Alzheimer’s Disease International, 2015.
[2] E. Barrett, and A. Burns, Dementia Revealed. What Primary Care Needs to Know. Department of
Health UK, 2014.
[3] T. Panch, P. Szolovits, and R. Atun, "Artificial intelligence, machine learning and health systems", J.
Glob. Health, vol. 8, no. 2, p. 020303, 2018.
[http://dx.doi.org/10.7189/jogh.08.020303] [PMID: 30405904]
[4] K.H. Yu, A.L. Beam, and I.S. Kohane, "Artificial intelligence in healthcare", Nat. Biomed. Eng., vol.
2, no. 10, pp. 719-731, 2018.
[http://dx.doi.org/10.1038/s41551-018-0305-z] [PMID: 31015651]
[5] Available from: https://adni.loni.usc.edu/
[6] C. Morgan, D.M. Ashcroft, E. Kontopantelis, D. Stamate, and D. Reeves, "Can dementia risk be
predicted using routine electronic health records. Society for Academic Primary Care Conference
(SAPC ASM)", 2019.
[7] J. Yoon, J. Jordon, and M. Schaar, "Gain: Missing data imputation using generative adversarial nets",
International Conference on Machine Learning, pp. 5689-5698, 2018.
[8] Ö. Yildirim, "A novel wavelet sequence based on deep bidirectional LSTM network model for ECG
Prognosis of Dementia Disease Prediction using Machine Learning 91
https://t.me/med1917
signal classification", Comput. Biol. Med., vol. 96, pp. 189-202, 2018.
[http://dx.doi.org/10.1016/j.compbiomed.2018.03.016] [PMID: 29614430]
[9] K.D. Spuhler, J. Gardus III, Y. Gao, C. DeLorenzo, R. Parsey, and C. Huang, "Synthesis of patient-
specific transmission data for PET attenuation correction for PET/MRI neuroimaging using a
convolutional neural network", J. Nucl. Med., vol. 60, no. 4, pp. 555-560, 2019.
[http://dx.doi.org/10.2967/jnumed.118.214320] [PMID: 30166355]
[10] D. Stamate, M. Kim, P. Proitsi, S. Westwood, A. Baird, A. Nevado-Holgado, A. Hye, I. Bos, S.J.B.
Vos, R. Vandenberghe, C.E. Teunissen, M.T. Kate, P. Scheltens, S. Gabel, K. Meersmans, O. Blin, J.
Richardson, E. De Roeck, S. Engelborghs, K. Sleegers, R. Bordet, L. Ramit, P. Kettunen, M. Tsolaki,
F. Verhey, D. Alcolea, A. Lléo, G. Peyratout, M. Tainta, P. Johannsen, Y. Freund-Levi, L. Frölich, V.
Dobricic, G.B. Frisoni, J.L. Molinuevo, A. Wallin, J. Popp, P. Martinez-Lage, L. Bertram, K.
Blennow, H. Zetterberg, J. Streffer, P.J. Visser, S. Lovestone, and C. Legido-Quigley, "A metabolite-
based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the
European Medical Information Framework for Alzheimer disease biomarker discovery cohort",
Alzheimers Dement. (N. Y.), vol. 5, no. 1, pp. 933-938, 2019.
[http://dx.doi.org/10.1016/j.trci.2019.11.001] [PMID: 31890857]
92 Disease Prediction using Machine Learning, 2024, 92-102
https://t.me/med1917
CHAPTER 6
A Clinical Decision Support System for Effective
Identification of the Onset of Asthma Disease
M.R. Pooja
1
Vidyavardhaka College of Engineering, Mysuru, Karnataka, India
Abstract: We present a clinical decision support system for the identification of
asthmatics in two different cohorts representing rural and urban populations in India.
The input data representing the two populations are cross-sectional in nature and are
necessarily categorical in nature, with information on clinical history emphasizing
clinical symptoms and patterns characterizing the disease. The system is described as
hybrid as it combines the unsupervised and supervised learning techniques in a unique
way as discussed in the work presented in the paper. The clustering information
emphasizing the phenotypic characterization of asthma is an input to the classifier and
a significant improvement is observed in the performance of the classifier. The results
of the developed hybrid decision support system are quite promising for suitable
deployment in a real-time scenario, as it explores the benefits of both supervised and
unsupervised learning techniques. Further, the use of clustering information in the form
of cluster evaluation scores as an input parameter to the classifiers can efficiently
predict disease outcomes, especially with diseases such as asthma, as the disease is
heterogeneous and exhibits several disease subtypes and heterogeneous phenotypes.
1,*
Keywords: Correlation, Hybrid, ISAAC, MFCM, Subject clustering.
INTRODUCTION
Asthma, being one of the most chronic respiratory diseases of the lungs is known
to be affected by various factors including, genetic and environmental features
that play a vital role in the persistence and progress of the disease [1]. Clinical
symptoms and their patterns along with their frequency have a great impact on the
disease outcome and impact when assessed using electronic health record data [2].
ISAAC is a well-recognized body that has formulated core questionnaires with
respect to asthma and its comorbidities that have been received well in most of the
pilot studies conducted across several regions. A hybrid decision support system
that operates in two stages has been proposed, wherein the results generated dur-
*
Corresponding author M.R. Pooja: Vidyavardhaka College of Engineering, Mysuru, Karnataka, India;
E-mail: pooja.mr@vvce.ac.in
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
All rights reserved-© 2024 Bentham Science Publishers
Соседние файлы в папке Библиотека им академика М.И. Перельмана