nebelle/HealthEdge}).  Recursive Feature Elimination, Cross-Validated (RFECV) feature selection algorithm, and Synthetic Minority Oversampling Technique (SMOTE) balancing are used because they showed efficient performance \cite{chawla2002smote}. RFECV \cite{guyon2002gene}  selects the best subset of features by removing some features and selecting the best subset based on a cross-validation score.  SMOTE \cite{chawla2002smote} adds synthetic data points by selecting random samples of the minority class and choosing a point in between these points and one of their k-nearest neighbors. We evaluated those models using different evaluation metrics such as Accuracy, F-measure, precision, recall, and AUC.

\subsection{Datasets}

Tables \ref{table:datasets characteristics} and \ref{table:datasets features} show the characteristics of the datasets and their corresponding features used to evaluate our proposed system respectively:  1) PIMA India \cite{smith1988using} from the National Institute of Diabetes and Digestive and Kidney Diseases,  2) Sylhet \cite{islam2020likelihood} was collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh, and 3) MIMIC III \cite{johnson2016mimic}, a large dataset which contains information of over 40,000 patients who stayed in critical care units of the Berth Israel Deaconess Medical Center between 2001 and 2012.

\begin{scriptsize}
\begin{longtable}{|p{2cm}|p{2.5cm}|p{2.5cm}|p{2.5cm}|p{2.5cm}|}
\caption{Original Datasets Characteristics.}
\label{table:datasets characteristics}\\
\hline
\rowcolor{Gray}
\textbf{Dataset} & \textbf{\# of Features} & \textbf{Positive Classes} & \textbf{Negative Classes} & \textbf{Total Records}\\
\hline

PIMA Indian
& 8
& 268 (34.9\%)
& 500 (65.1\%)
& 768 \\ \hline

Sylhet
& 16
& 320 (61.5\%)
& 200 (38.5\%)
& 520 \\ \hline

MIMIC III
& 4
& N/A
& N/A
& 46,520 \\ \hline	 		

\end{longtable}
\end{scriptsize}


\begin{scriptsize}
\begin{longtable}{|p{4cm}|p{4cm}|p{4cm}|}
\caption{Features of Datasets under Experiment.}
\label{table:datasets features}\\
\hline
\rowcolor{Gray}
\textbf{Sylhet} & \textbf{PIMA Indian} & \textbf{MIMIC III} \\ \hline

Age	& Pregnancies	& Ethnicity\\ \hline	 		

Gender	& Glucose 	& Gender\\ \hline	 		

Polyuria$^{1}$ & Blood Pressure & Age\\ \hline	 		

Polydipsia$^{2}$ & Skin Thickness	& Family History of Diabetes\\ \hline	 		

sudden weight loss & Insulin & \\ \hline	 		

weakness	& BMI & \\ \hline	 		

Polyphagia$^{3}$ & Diabetes pedigree$^{4}$ & \\ \hline	 		

Genital thrush$^{5}$ 	& Age	& \\ \hline	 		

visual blurring	& & \\ \hline	 		

Itching	& & \\ \hline	 		

Irritability & & \\ \hline	 		
		
delayed healing & & \\ \hline	 		
		
partial paresis$^{6}$ & & \\ \hline	 		
	
muscle stiffness & & \\ \hline	 		
	
Alopecia$^{7}$ & & \\ \hline	 		

Obesity & & \\ \hline	 		

\multicolumn{3}{p{12cm}}{$^{1}$Polyuria is a condition where the body urinates more than usual and passes excessive or abnormally large amounts of urine each time you urinate \cite{islam2020likelihood}} \\

\multicolumn{3}{p{12cm}}{$^{2}$Polydipsia is the feeling of extreme thirstiness \cite{islam2020likelihood}} \\

\multicolumn{3}{p{12cm}}{$^{3}$Polyphagia, also known as hyperphagia, is the medical term for excessive or extreme hunger \cite{islam2020likelihood}} \\

\multicolumn{3}{p{12cm}}{$^{4}$Diabetes pedigree provides a synthesis of diabetes history in relatives and the genetic relationship of those relatives to the subject \cite{smith1988using}} \\

\multicolumn{3}{p{12cm}}{$^{5}$Genital thrush is a common infection caused by an overgrowth of the yeast \cite{islam2020likelihood}} \\

\multicolumn{3}{p{12cm}}{$^{6}$Paresis involves the weakening of a muscle or group of muscles. It may also be referred to as partial or mild paralysis. Unlike paralysis, people with paresis can still move their muscles. These movements are just weaker than normal \cite{islam2020likelihood}.} \\

\multicolumn{3}{p{12cm}}{$^{7}$Alopecia areata is an autoimmune disorder that causes your hair to come out, often in clumps the size and shape of a quarter \cite{islam2020likelihood}}

\end{longtable}
\end{scriptsize}

\subsection{Data Exploration}

PIMA Indian dataset shows a number of missing values in some numerical features. In particular, Blood Pressure, Skin Thickness, and BMI are characterized by a heavy weight for the \textit{'zero'} value, on, shown in Figure \ref{fig:data exploration PIMA indian}.  This implies that the corresponding observations should be removed at the preprocessing stage.

\begin{figure}
\centering
  \includegraphics[width=\linewidth]{Data exploration PIMA Indian.png}
  \caption{Data Exploration Histograms for PIMA Indian Dataset Numerical Features.}
  \label{fig:data exploration PIMA indian}
\end{figure}

For each dataset, we study the correlations among features and the diabetic/non-diabetic class. We choose the Phik ($\Phi$k) correlation coefficient because that works consistently between categorical, ordinal, and interval variables. It captures non-linear dependency and reverts to the Pearson correlation coefficient in case of bi-variate normal input distribution \cite{baak2020new}. So, it encompasses multiple types of correlations.  As shown in Figure \ref{fig:correlation PIMA indian}, PIMA India presents a logical correlation between Age and Number of Pregnancies.  Regarding diabetes detection, the features that are correlated with the diabetic/non-diabetic outcome of the patient are Glucose, Age, BMI, Insulin, and Skin Thickness.  In addition, BMI is correlated with Blood Pressure. Correlations for Sylhet are displayed in Figure \ref{fig:correlation SYLHET}.  In this dataset, the diabetic/non-diabetic outcome is highly correlated with Polydipsia and Polyuria and in a lower manner with partial paresis, Gender, and sudden weight loss. Furthermore, Polydipsia and Polyuria are highly correlated with each other.  Similar to PIMA India, MIMIC III dataset shows high correlations between the class (diabetic/non-diabetic) outcome and the Age feature (Figure \ref{fig:correlation MIMIC III}). However, Figure \ref{fig:correlation MIMIC III} shows a correlation between Age and Ethnicity that may indicate the randomness in the MIMIC III dataset under study.

\begin{figure}
\centering
\begin{subfigure}{0.5\textwidth}
    \includegraphics[width=\textwidth]{Correlation PIMA Indian.png}
    \caption{PIMA Indian}
    \label{fig:correlation PIMA indian}
\end{subfigure}
\hfill
\begin{subfigure}{0.5\textwidth}
    \includegraphics[width=\textwidth]{Correlation SYLHET.png}
    \caption{Sylhet}
    \label{fig:correlation SYLHET}
\end{subfigure}
\hfill
\begin{subfigure}{0.5\textwidth}
    \includegraphics[width=\textwidth]{Correlation MIMIC III.png}
    \caption{MIMIC III}
    \label{fig:correlation MIMIC III}
\end{subfigure}
\caption{Correlation between Features and Diabetic/Non-diabetic Class for PIMA Indian, Sylhet, and MIMIC III Datasets}
\end{figure}

\subsection{Data Preprocessing}

In P