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\begin{document}


\def\RunningAuthor{Mahendran Radha et al.}
\firstPage{374}
\articleType{Original Article}
\receivedDate{15 Nov 2020}
\acceptedDate{15 Dec 2020}
\revisedDate{12 Dec 2020}
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\title{\textbf{A detailed investigation in determining Alzheimer's disease and its risk factor using different classification techniques}}
\author{Mahendran~Radha\textsuperscript{*},  
            Anitha~M, 
            Jeyabaskar~Suganya~\\[5pt]{Department of Bioinformatics, School of Life Sciences, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, Tamil Nadu, India}}

\begin{abstract}
The prevalence of genetic disorders has recently crept surprisingly high. Neurodegenerative complications, specifically, pose physical and mental stress to parents and caretakers. These complications may be witnessed in the case of dementia. The general dementia type that accounted for between 60 to 80 per cent of psychiatric illnesses was Alzheimer's disease. At an earlier stage, illness detection serves as a critical task that helps the diseased person to enjoy a decent quality of life. It has become a much necessitated strategy towards relying on automated techniques like data mining approach for early diagnosis and assessment of risk factors concerned with Alzheimer's. There has been an unprecedented growth of interest concerned with devising novelized approaches proposed in recent times for classifying the disease. However, there is still a grave need for developing an efficacious approach for better prognosis and classification. Data mining is carried out using different machine-learning approaches to assess the risk factors for Alzheimer's disease. Through the present research, and we compared numerous classification methods such as Decision Tree, Linear SVM, KNN, Logistic Regression, Radial SVM, and Random Forest, and finally reported the most outstanding approach in terms of its accuracy.
\end{abstract}\def\keywordstitle{Keywords}

\begin{keywords}Alzheimer's Disease,\newline Dementia,\newline Classification Techniques,\newline Linear SVM,\newline WEKA
\end{keywords}
\twocolumn[ \maketitle {\printKwdAbsBox}]
\makeatletter{\raggedright\textsuperscript{*}Corresponding Author\par Name:\ Mahendran~Radha~\\ Phone:\ +91 9003237145~\\  Email:\ mahenradha@gmail.com
        \par}\vspace*{-11pt}\hrulefill\par{\fontsize{12}{14}\selectfont ISSN: 0975-7538}\par% 
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\section{Introduction}
Approximately 44 million people have dementia \unskip~\citep{971718:21118413}.  There are 38 million people with Alzheimer's disease who are struggling. One of the forms of dementia is Alzheimer's disease \unskip~\citep{971718:21118416,971718:21118421}. Alois Alzheimer's, a German neurologist and physician, discovered Alzheimer's disease in 1906 \unskip~\citep{971718:21118417}. Multiple risk factors that lead to the progression of the disease are distinct \unskip~\citep{971718:21118413}. Height, Down syndrome, consumption of alcohol and smoke, food style, cholesterol, etc. The signs of this disorder are interpersonal coordination, decision making, total lack of memory and failure of gestures, bad judgment, and irregular moods. The three different steps of ADD care are visiting the general surgeon, doing neuropsychological assessments, and taking MRI scans \unskip~\citep{971718:21118424}. By 2001, more than 11 million people were uniformly afflicted by Alzheimer's disease. There are approximately about 36 (35.6) million people suffered with AD or other manifestations of dementia, rising to about 66 (65.7) million by 2030 and rising to around 115 (115.4) million by 2050 \unskip~\citep{971718:21118415}. The number of people with dementia is predicted to double by 2030 and to triple by 2050. Although as medical specialists such as physicians, there is a significant difference between them; medical practitioner never reveals to the outside world their system of prediction of a specific illness. Therefore this crisis could be overcome by a prediction approach with expert experience and lead to reliable disease prediction outcomes. We use different kinds of machine learning algorithms for this research.

\textbf{Literature survey}

In over 60 to 80\% of dementia cases, Alzheimer's disease accounted. Such disorders remain undiagnosed at an early stage \unskip~\citep{971718:21118426,971718:21118420}. There are 3 main diagnostic stages via a general practitioner. Step one is consultation. The 2nd stage includes multiple neuropsychological assessments after MRI scans are taken in the 3rd stage \unskip~\citep{971718:21118422}. AD requires a screening test can be used, regardless of culture, gender, education and religion, for the subjects. The Dementia Research Group 10/66 formed a network in 1998 and dedicated itself to studies of the highest standards in those areas. This phase is also dependent on the psychologist's mood. In addition it is not easy to prevent human error. This crisis could be resolved by machine based research. So, researchers discovered information using a data mining approach. Using methods such as analytics, artificial intelligence and machine learning, data mining can be performed. As different scholars have used data mining was explored for the study of various diseases \unskip~\citep{971718:21118413}. Use decision tree and Bayesian classification when evaluating the data sets of patients with heart disease \unskip~\citep{971718:21118425}. Classification algorithms have been used to classify Parkinson's disease \unskip~\citep{971718:21118410}. The machine learning approach used in the classification of Alzheimer's disease, vascular dementia and Parkinson's disease \unskip~\citep{971718:21118419}. The whole work illustrates the efficacy of assuming that the risk factor for proper classification of AD, VD and PD is very significant. It was determined from assessing 180 related investigations. The study showed a precision of 99.33 per cent obtained using perceptron multilayer and random forest \unskip~\citep{971718:21118410}. The machine learning investigation governing Alzheimer's disease was discussed in \unskip~\citep{971718:21118418}.
    
\section{Materials and Methods}
\textbf{Architectural framework}

The workflow of the present study is represented in Figure~\ref{f-65fb9ee9173e}.

\textbf{Dataset Array}

The data obtained here is the most significant. The 750 medical reports obtained by various neuro-psychologists comprised of datasets. The four age ranges are 65-69, 70-75, 76-79, and over 80 years of age.


\bgroup
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\centering \makeatletter\IfFileExists{images/9df9eef5-822d-443b-be2e-cd57794aeb3e-upicture3.png}{\includegraphics{images/9df9eef5-822d-443b-be2e-cd57794aeb3e-upicture3.png}}{}
\makeatother 
\caption{\boldmath {Block diagram indicating the working flow.}}
\label{f-65fb9ee9173e}
\end{figure}
\egroup
\textbf{Preprocessing}

It is a stage the missing and incorrect values can be verified. Data preprocessing is not carried out here as there is no risk of missing data.\textbf{\space }

\textbf{Options for Attributes}

A selection of attributes is the main stage, and certain attributes create a great difference in decision making. The data set comprises 8 attributes that represent the main risk factors related to AD namely, Family history, Age, Environmental toxins, Gender, Head injury, Factors including High BP and cholesterol level, Low education Level, and Lifestyle.


\bgroup
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\centering \makeatletter\IfFileExists{images/e692f920-f648-4db3-82a8-5c47871bae04-upicture2.png}{\includegraphics{images/e692f920-f648-4db3-82a8-5c47871bae04-upicture2.png}}{}
\makeatother 
\caption{\boldmath {Correlation matrix representing the datasets.}}
\label{f-e5e6d974bd98}
\end{figure}
\egroup
\textbf{Classification Techniques (classifiers)}

\textbf{WEKA Tool}

The next stage is the grouping. This is done to understand just how the material is being categorized. 


\begin{table*}[!htbp]
\caption{\boldmath {Accuracies before standardization.} }
\label{tw-7505efbab766}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.4834\linewidth-2\tabcolsep}p{\dimexpr.5166\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Model & \cAlignHack Accuracy\\
\tblmidrule 
\cAlignHack Radial SVM &
  \cAlignHack 0.6510416666666666\\
\cAlignHack KNN &
  \cAlignHack 0.7291666666666666\\
\cAlignHack Decision Tree &
  \cAlignHack 0.7552083333333334\\
\cAlignHack Linear SVM &
  \cAlignHack 0.7708333333333334\\
\cAlignHack Logistic Regression &
  \cAlignHack 0.7760416666666666\\
\cAlignHack Random Forest &
  \cAlignHack 0.8072916666666666\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Accuracy after standardization and selecting correlated features.} }
\label{tw-0d375844d94f}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.21270000000000003\linewidth-2\tabcolsep}p{\dimexpr.27279999999999994\linewidth-2\tabcolsep}p{\dimexpr.24380000000000003\linewidth-2\tabcolsep}p{\dimexpr.2707\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Model & \cAlignHack New Accuracy & \cAlignHack Accuracy & \cAlignHack Increase\\
\tblmidrule 
Linear Svm &
  \cAlignHack 0.78125 &
  \cAlignHack 0.7708333333333334 &
  \cAlignHack 0.01041666666666663\\
Radial Svm &
  \cAlignHack 0.7708333333333334 &
  \cAlignHack 0.6510416666666666 &
  \cAlignHack 0.11979166666666674\\
Logistic Regression &
  \cAlignHack 0.7760416666666666 &
  \cAlignHack 0.7760416666666666 &
  \cAlignHack 0.0\\
KNN &
  \cAlignHack 0.7291666666666666 &
  \cAlignHack 0.7291666666666666 &
  \cAlignHack 0.0\\
Decision Tree &
  \cAlignHack 0.7291666666666666 &
  \cAlignHack 0.7552083333333334 &
  \cAlignHack -0.02604166666666674\\
Random Forest &
  \cAlignHack 0.7708333333333334 &
  \cAlignHack 0.8072916666666666 &
  \cAlignHack -0.03645833333333326\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Cross Validation Scores.} }
\label{tw-a141fadf5997}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.5062\linewidth-2\tabcolsep}p{\dimexpr.4938\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Model & \cAlignHack CV Mean\\
\tblmidrule 
\cAlignHack Linear SVM &
  \cAlignHack 0.78125\\
\cAlignHack Radial SVM &
  \cAlignHack 0.7708333333333334\\
\cAlignHack Logistic Regression &
  \cAlignHack 0.7760416666666666\\
\cAlignHack KNN &
  \cAlignHack 0.7291666666666666\\
\cAlignHack Decision Tree &
  \cAlignHack 0.7291666666666666\\
\cAlignHack Random Forest &
  \cAlignHack 0.7708333333333334\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}
For research, the WEKA tool is used. The classification algorithm runs several times to maximize precision. WEKA has two successful assessors for learning. The first one is a classifier and cross-validation is the second one.

\textbf{Linear } \textbf{SVM (Linear support vector machine)}

Linear SVM is the recently discovered classification technique for large dataset data mining. Compare to other techniques, Linear SVM is the best performer.

\textbf{Radial } \textbf{SVM (Radial support vector machine)}

Radial SVM is a common kernel feature used in different learning algorithms that are kernelized. It is commonly used in the Methodology of Support Vector Machines \unskip~\citep{971718:21118414}. Do the optimization of an SVM model that can forecast bankruptcy. While the RBF kernel is commonly used in the fitting of data for its stability, other common kernels, such as polynomial or sigmoid, are \unskip~\citep{971718:21118419}.

\textbf{Logistic Regression}

For predicting binary classes it is statistical method. The target variable is dichotomous. Dichotomous means there are only two possible classes. It calculates the probability of an event occurrence.

\textbf{KNN (K Nearest Neighbor)}

The K Nearest Neighbor algorithm has been used in

various data analysis because of its simplicity and high accuracy \unskip~\citep{971718:21118412}. It has been accepted as one of top 10 algorithms in data mining. Estimating k value by 10 fold cross validation, 97.4\% of accuracy has been obtained \unskip~\citep{971718:21118411}.

\textbf{Decision Tree}

It is a tree structure that is flowchart like. When the internal node represents a function (or attribute), the branch represents a law of choice, and the outcome is expressed by each leaf node. In decision making, this flowchart-like form supports. Like a flowchart map, it's a hallucination that imitates thinking at the human level. But it is easy to grasp and interpret only decision trees.

\textbf{Random Forest}

It is a managed algorithm for learning. For classification and regression, Random Forest is used. The algorithm is the algorithm that is most versatile and simple. Woodland is composed of trees. It lies at the base of the Boruta algorithm, which is a dataset that selects essential attributes.
    
\section{Results and Discussion}
From the observed classification methods that are used for the datasets (i.e.) Risk factors which are major contributors for AD were taken into account. The accuracy from the classification of algorithms before standardization was represented in Table~\ref{tw-7505efbab766}. The percentage of test set tuples that are appropriately identified by the classifier is the accuracy of the classifier on a given test set. Post-standardization precision and collection of correlation features are calculated in Table~\ref{tw-0d375844d94f} and its cross-validation scores represented in Table~\ref{tw-a141fadf5997}. From the result, the process was used for determining the model showcasing the best accuracy. From the determined Datasets of AD based on major risk factors is represented in the Correlation matrix Figure~\ref{f-e5e6d974bd98}.
    
\section{Conclusions}
Different data mining classification methods have been compared and graded. The specificity of the execution of each procedure is observed. Linear Support Vector Machine, Radial Support Vector Machine, Logistic Regression, K Nearest Neighbor, Decision Tree, and Random Forest are the following classifiers used in the prediction of Alzheimer's disease risk factors. Among them, compared with other classifiers, Linear SVM demonstrated better accuracy. This study shows the Linear SVM classification method serves as the best protocol for the prediction of various genetic disorders. We conclude with this analysis linear SVM classification technique can use another genetic disease risk prediction process.

\textbf{Conflict of Interest}

The authors declare that they have no conflict of interest for this study.

\textbf{Funding Support}

The authors declare that they have no funding support for this study.
    

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