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Alzheimer Disease Prediction using Machine Learning Algorithms
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Alzheimer Disease Prediction using Machine Learning Algorithms
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1084 Alzheimer Disease Prediction using Machine Learning Algorithms Mohammed oveze1, Sangeetha mandal2, Rumana Kouser3, Melvin Mickle4, Kiranashree B.K5 1,2,3,4 Students, Department of Computer Science & Engineering, T John Institute of Technology, Bengaluru, India 5 Asst. Professor, Department of Computer Science & Engineering, T John Institute of Technology, Bengaluru, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this study, machine learning techniques are used to forecast Alzheimer's. Among neurodegenerative illnesses Although the symptoms are first mild, they worsen over time. A typical dementia is Alzheimer's disease. Psychological factors like as age, the frequency of visits, the MMSE, and education can be used to predict the AD Key words: Alzheimer disease, mild cognitive impairment Machine learning algorithms, psychological parameters 1. INTRODUCTION Short-term memory loss, paranoia, and delusional thoughts are symptoms of Alzheimer's Disease (AD), a degenerative neurological disorder that is often misdiagnosed asstressor aging-related symptoms. About 5.1 million Americans are afflicted by this disease. AD does not receive adequate medical care. AD must be treated with medication consistently. Because AD (1) is chronic, it might last for a long time or for the rest of your life. Therefore, in order to prevent significant brain damage, it is crucial to prescribe medication at the right time. Since we need to gather a lot of data, apply advanced methods for prediction, andconsultan experienced doctor, early detection of this disease is a time- consuming and expensive process. 1.1 Motivation Innovative approaches such as machine learning are increasingly being used to offer prescient and customized prescriptions. Viewingmedical reportsmayleadradiologists to miss other disease conditions because it only considers a few causes and conditions. The goal here is to identify the knowledge gaps and potential opportunitiesassociatedwith ML and EHR derived data. 1.2 Objectives This project is being putforthtoforecastAlzheimer'sdisease prediction and to acquire better and accurate results. It will use a CNN algorithm and SVM algorithm Python programming language is employed for machine learning in order to complete this operation. 1.3 Problem Statement There is no proper awareness about Alzheimer Disease. As they age, they may experience changes in your physical abilities and walking, sitting, and eventually swallowing. Individuals may need substantial assistance with daily activities as their memory, and cognitive skills continue to decline. At this stage, individuals may need 24/7 assistance for personal care and daily activities. When people suffer from dementia, their ability to communicate, adapt to their environment, and eventually move is lost. It becomes much more difficult for them to communicate pain through words or phrases. 1.4 Machine Learning Using Python Python is a sophisticated, widely used programming language. In 1991, "GUIDO VAN ROSSUM" invented it. Numerous libraries, including pandas, numpy, SciPy, matplotlib, etc., are supported by Python. It supports Xlsx, Writer, and X1Rd, among other packages. Complex performed extremely effectively using it. There are numerous functional Python frameworks. Machine learning is a branch of artificial intelligence that allows computer frameworks to pick up new skill and enhance their performance with the help of data.Itis employedto research the development of computer-based algorithms for making predictions about data. Providing data is the first step in the machine learning process, after which the computers are trained by using a variety of algorithms to create machine learning models. Software engineering's branch of machine learning has significantly altered how people analyses data. 2. RELATED WORKS According to research [1] The most prevalent and common type of dementia is Alzheimer's disease (AD). AD can be clinically diagnosed by physical and neurological examination, so there is an need for developing better diagnostic tools for AD. MRI (Magnetic resonance imaging)scanswere processed byFree Surfer, a powerful tool suitable for processing and normalizing brain MRI images. Themultistageclassifierused in this thesis produced a good performance for AD detection as compared with previous individual machine learning approaches, such as SVM and KNN. Based on [2] In this paper, we have proposed a new classification framework based on combination of CNN and RNN to perform the longitudinal analysis of structural MR images for AD diagnosis. CNN model wasproposedtoextract the spatial features of each time point and generates single- time classification result, while RNN based on cascaded
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1085 BGRU was used to model the temporal variations an extract the longitudinal features for improving disease classification. Experimental results on the ADNI dataset demonstrate the effectiveness of the proposed classification algorithm. In the future works, wewill includeotherimaging features such as structural and functional connection networks of brain for RNN based longitudinal analysis. In addition, our work can be Experimental results show that the proposed method achieves classification accuracy of 91.33% for AD vs. NC and 71.71% for pMCI vs. sMCI, demonstrating the promising performance for longitudinal MR image analysis. The [3] Alzheimer's disease seriously affects the lives of the elderly and theirfamilies.Mildcognitiveimpairment(MCI)is a transitional state between normal aging and Alzheimer's disease. MCI is often misdiagnosed as the symptoms of normal aging, which results to miss the best opportunity of treatment. In this paper, the neuroimagingdiagnosisand the clinical psychological diagnosis are combined. The experimental results show that the proposed multi-modal auxiliary diagnosis can achieve an excellent diagnostic efficiency. The consistency of the output of two convolutional neural networks is judged by correlation analysis. If the results of the two CNN modelsaresimilar,itis intuitive that the diagnosis for the same patient are consistent with the difference modality diagnosis. The accuracy rates achieves 95.9% (CN vs. AD), 85.0%(CN vs. MCI), and 75.8% (MCI vs. AD), respectively . As in [7] The accurate diagnosis of Alzheimer's disease (AD) is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as MRI and PET, has shown its effectiveness for AD diagnosis. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binaryclassificationand multiclass classification tasks. Deep learning and deep polynomial networks. It can be found that MM-SDPN algorithm achieves the best performance with mean classification accuracy of 97.13%. In [10] 15 metabolites associated with cognition including sub fractions of high-density lipoprotein, docosahexaenoic acid, ornithine, glutamine, and glycoprotein acetyls. Six of the metabolites were related to the risk of dementia and lifestyle factors independent of classical risk factors such as diet and exercise. Measurements of cognitive function and blood drawn for metabolite measurementswereconcurrent in all metabolite measurements from our discovery and 73.6% of the samples in the replication. As with reference [8] "View aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi- modality data", Med. Image Anal., 2017. View-aligned hypergraph learning (VAHL) method to explicitly model the coherence among views. We evaluate our method on the baseline ADNI-1 database with 807 subjects and three modalities. Experimental results show that our method outperforms state-of-the-art methodsforAD/MCIdiagnosis. We develop a view-aligned hypergraph classification model to explicitly capture the underlying coherenceamongviews, as well as automatically learn the optimal weights of different views from data. Results on the baseline ADNI-1 database with MRI, PET, andCSFmodalitiesdemonstratethe efficacy of our method in AD/MCI diagnosis. this paper, they propose a view-aligned hypergraph learning (VAHL). By using VAHL accuracy of 78.9%. According to [10] In this paper, the authors developed a system to improve the prediction of progression to Alzheimer’s Disease (AD) among older individuals with mild cognitive impairment. The dataset used was the ADNI dataset for predicting the progression of AD. PHS, Atropy score and MMSE predictor algorithm were used for the prediction of the progression, highest accuracy of 78.9% along with a sensitivity of 79.9% was found when all three predictor algorithms were used together. 3. IMPLEMENTATION AND WORKING 3.1 Architectural Diagram The structural basic working methodology is based on the flowchart given above. 3.2 Data Collection and Data Cleaning
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1086 A decision tree is a supervised learning model that usesa set of rules to find a solution. A proportion of people affected by AD according to ages in the United States. Of course, the image search process requires two processes. step: In the first stage we generate the features and then reproduces the query image and subsequent steps correlate these characteristics with those already recorded in the database [2]. The selection uses the PSO algorithm to create the best biomarker indicative of AD or MCI. Data is Acquired from Alzheimer's Disease Neuroimaging Initiative (ADNI) Database. Control Based Image Retrieval was used for retrieving images from the database feature selection includes volume and thickness measurement. Then the best feature list is obtained from the PSO algorithm [2]. control- based image search was used to retrieve images from the database. Then use 3D Convolutional neural network to perform feature learning. CNN is followed via the pooling layer there are many ways to pool or otherwise collect the maximum value, or a specific sequence of neurons within a section. 3.3 Data Preprocessing and Analysis of Data First pre-processed MRI images are created after recording the database. Ruoxuan Cuia et al. provided a model that performs longitudinal analysis which is performed sequentially and is required for IRM design and calculation. Disease progression over time for: more accurate diagnosis [3]. actual process used Features of brain morphological abnormalities and longitudinal differences in MRI and a classifier is built to distinguish between different groups. The classification model consists of the early diagnosis, initially preprocessing of raw R-fMRI is done. 3.4 Data Visualization Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. Data visualization is used in many high- quality visual representations. 3.5 Cross Validation and Training the Model To train a machine learning model using a subset of the dataset, cross-validation is performed. training is important to achieve accuracy when dividing the data setinto setof"N" for the evaluation of the model has been built. We need to train the model first because the data is divided in two modules: a test set and a training set. The Target variable is part of the training set. The training dataset is according to the decision tree regression method. Using onedecisiontree to generate regression model. When finite amount of data cases is taken, k-fold cross verification was implemented mainly to avoid the over fitting complication. 3.5 Testing and Integration with UI A web framework like Flask provides us with technology, tools, and the libraries neededtocreatewebapplication. The bottle is one framework mainly used to integrate Python models because it is easy to build routes together. Alzheimer's disease is predicted using training model and test data set. The front is then connected to the model which is trained using Python's Flask framework.Aftercreatingthe model and successfully creatingthedesired results, followed by integration with the user interface (UI) phase, then flask is used. 4.CONCLUSION The current study demonstrates that the age-sensitive PHS and structural neuroimaging can be combined to more accurately predict the clinical progression to AD in MCI patients and basic mental capacity. Improved individual assessments of AD risk among elderly patients presenting with subjective memory complaints may be useful inclinical practice to guide treatment plans. These assessments are also extremely important for intervention studies, where recruiting high-risk subjects at an early stage of the disease process is crucial for evaluating the efficacyofnovel disease- altering intervention. 5. ACKNOWLEGEMENT We thank, Dr. Thomas P John (Chairman), Dr. Suresh Venugopal P (Principal), Dr Srinivasa H P (Vice-principal), Ms. Suma R (HOD – CSE Department), Dr. John T Mesia Dhas (AssociateProfessor&ProjectCoordinator),Ms.Kiranashree B.K (Assistant Professor & Project Guide), Teaching & Non- Teaching Staffs of T. John Institute of Technology, Bengaluru – 560083. REFERENCES [1] K.R.Kruthika,Rajeswari,H.D. 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of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1087 [4] Fan Zhang, Zhenzhen Li, Boyan Zhang, Haishun Du , Binjie Wang , Xinhong Zhang, “Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease”, Neuro Computing, 2019. https://doi.org/10.1016/j.neucom.2019.04.093 [5] Chenjie Ge , Qixun Qu , Irene Yu-Hua Gu , Asgeir Store Jakola “Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images”, Neuro Computing, 2019. https://doi.org/10.1016/j.neucom.2019.04.023 [6] Tesi, N., van der Lee, S.J., Hulsman, M.,Jansen,I.E.,Stringa, N., van Schoor, N. et al, “Centenarian controls increase variant effect sizes by an average twofold in an extremecase extreme control analysis of Alzheimer's disease”, Eur J Hum Genet. 2019; 27:244–253 https://doi.org/10.1038/s41431-018-0273-5 [7] J. Shi, X. Zheng, Y. Li, Q. Zhang, S. Ying, "Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease", IEEE J. Biomed. Health Inform., vol. 22, no. 1, pp. 173- 183, Jan. 2018. https://doi.org/10.1109/JBHI.2017.2655720 [8] M. Liu, J. Zhang, P.-T. Yap, D. Shen, "View aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modalitydata",Med.ImageAnal.,2017 vol. 36, pp. 123- 134. https://doi.org/10.1016/j.media.2017.10.005 [9] Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, “CSF biomarkers of Alzheimer’s disease concordwith amyloid-bPET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts”. Alzheimer’s Dement 2018; 14:1470–81. https://doi.org/10.1016/j.jalz.2018.01.010 [10] Van der Lee SJ, Teunissen CE, Pool R, Shipley MJ, Teumer A,Chouraki V, “Circulating metabolites and general cognitive abilityand dementia: Evidence from 11 cohort studies”, Alzheimer’s Dement2018;14:707–22 . https://doi.org/10.1016/j.jalz.2017.11.012 [11] Kauppi Karolina, Dale Anders M, “Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease”, Frontiers in Neuroscience, 2018. https://doi.org/10.3389/fnins.2018.00260 [12] Grassi M, Loewenstein DA, Caldirola D, Schruers K, Duara R, Perna G, “A clinically-translatablemachinelearning algorithm for the prediction of Alzheimer's disease conversion: further evidence of its accuracy via a transfer learning approach”, Int Psychogeriatr, 2018 14:19. https://doi.org/10.1017/S1041610218001618 [13] Nation, D.A., Sweeney, M.D., Montagne, A., Sagare, A.P., D’Orazio, L.M., Pachicano, M. et al, “Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction”, Nat Med. 2019;25:270–276. https://doi.org/10.1038/s41591-018-0297-y [14] G. Chen, B. D. Ward, C. Xie, et al. “Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging,”Radiology,vol.259, no. 1, pp. 213-221, 2011. https://doi.org/10.1016/j.imu.2019.100248 [15] R. Cuingnet, E. Gerardin, J. Tessieras, et al. “Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods usingtheADNI database,” Neuro image, vol. 56, no. 2, pp. 766-781, 2011. https://doi.org/10.1016/j.imu.2020.100339 [16] O. Firat, L. Oztekin and F. T. Y. Vural, “Deep learning for brain de-coding,” Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, pp. 2784-2788, 2014. https://doi.org/10.1109/ICIP.2014.7025563 [17] S. Liu, S. Liu and R. Kikinis, “Early diagnosis of Alzheimer’s diseasewithdeeplearning,”Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on. IEEE, pp. 1015-1018, 2014. https://doi.org/10.1109/ISBI.2014.6868045 [18] D. H. Ye, K. M. Pohl and C. Davatzikos, “Semi-supervised pattern classification application to structural MRI of Alzheimer’s disease,” Pattern Recognition in Neuroimaging (PRNI), 2011 International Work-shop on. IEEE, pp. 1-4, 2011. https://doi.org/10.1109/PRNI.2011.12 [19] C. Wee, P. Yap, K. Denny, J. N. Browndyke, G. G. Potter, K.A. Welsh-Bohmer, L. Wang, and D. Shen, “Resting-state multi-spectrum functional connectivity networks for identification of MCI patients,” PLoS one, vol. 7, no. 5, e37828, 2012. https://doi.org/10.1371/journal.pone.0037828 [20] C. Sorg, V. Riedl, M. Mhlau, et al, “Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease,” Proceedings of the National Academy of Sciences, vol. 104, no. 47, pp. 18760-18765, 2007. https://doi.org/10.1073/pnas.0708803104
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