[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118740-en":3,"doc-seo-118740-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118740,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Prediction of Dementia Using Machine Learning Model and Performance Improvement with Cuckoo Algorithm","Dementia is a major brain disorder, and the study aims to predict dementia using the open-access OASIS imaging dataset. Feature engineering is emphasized to prepare inputs for machine learning, followed by modeling with Gaussian naïve Bayes, support vector machine, and linear regression. Model validation uses accuracy, precision, recall, and F1-score, with the top results reaching 95%, 97%, 95%, and 95%. Performance is further improved by applying the cuckoo algorithm to remove factors that degrade prediction quality.","Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm  \nSivakani Rajayyan1, Syed Masood Mohamed Mustafa2  \n1Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India 2Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India  \n\n| Article history:\u003Cbr>Received Jun 30, 2022 Revised Sep 16, 2022 Accepted Oct 1, 2022 | Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO) . Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95% . The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Classification Dementia\u003Cbr>Gaussian naïve Bayes Linear regression Support vector machine |  |\n\nCorresponding Author:  \nSyed Masood Mohamed Mustafa  \nDepartment of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur, Chennai, India  \n[Email: ms.masood@crescent.education](Email: ms.masood@crescent.education)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe process of converting the raw data into useful data as required is called data mining. The paper gives the contribution of feature engineering and the prediction of dementia using machine learning models [1] . The features are very important for a good prediction. The data analyzer should decide what kind of data and in which format it is required; for this, feature engineering is the focus. Feature engineering is the process of making the dataset in such a way that is required for processing using machine learning models [2], so that the prediction done using the machine learning model will be the best and most accurate. The process of creating new features from the existing raw data for processing is called feature creation [3] . The required feature for good prediction can be created using data mining tools. The process of changing the data to another required format is called transformation. Data encoding is applied to convert the text to numeric values for the instances in the dataset. The process of extracting the required feature from the dataset is called feature extraction. Each and every data will be processed and based on the importance of the data the feature will be extracted. The process of selecting the required feature is called feature selection [4]. Feature selection is an important process in feature engineering.  \nA machine learning model is a model constructed using the machine learning algorithm for the prediction of results using an input dataset. The machine learning model can be mainly classified into supervised and unsupervised learning models [5] . The supervised learning model trains the labeled data  \nand predicts the result. The unsupervised learning model trains the unlabeled data","cbCaiq6BleleFact","https://ap.wps.com/l/cbCaiq6BleleFact","pdf",603320,1,10,"English","en",105,"# Article Info\n# Abstract\n# 1. 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