## 6 Final Remarks  
As aforementioned, GAs have been developed to find good classification rules. Based on that, it is important to keep improving these methods to achieve better results. In this work, a refinement of the NLCEE was proposed, changing its chromosomal structure by a binary representation. The proposed method was executed using five medical domain datasets compared with two GAs and four traditional classifiers. The results showed that the proposed BIN-NLCEE achieved better results than traditional classifiers. When compared with the GAs, the BIN-NLCEE achieved better fitness values and converge rates. Therefore, it is possible to conclude that the binary representation for these datasets can improve GA performance. As mentioned before, the proposed method provides a rule set by the end of its execution, thus allowing the specialist to choose the rule based on its fitness and interpretability. Thus, the BIN-NLCEE can be considered a good specialist-oriented method. As future works, we hope to test the proposed GA with a higher number of bits in its binary representation, build IF-THEN rules in different and more complex datasets (real-world medical and biological datasets), and test other genetic parameters, such as mutation rate. Also, we would like to implement and investigate a new approach to \begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & **Rule** \\ \hline \multirow{7}{*}{Breast-w} & IF((A1 \(<\) 0.0625 OR A1 \(>\) 0.4219)AND(A2 \(\geq\) 0.2657) \\  & AND(A3 \(\geq\) 0.1719)AND(A7 \(\geq\) 0.0625) \\  & AND(A8 \(\geq\) 0.1563)AND(A18 \(<\) 0.5938) \\  & AND(A20 \(<\) 0.6875)AND(A21 \(<\) 0.7188) \\  & AND(A30 \(<\) 0.7813)) THEN M \\  & IF((A15 \(<\) 0.8438)AND(A16 \(<\) 0.3125) \\  & AND(A17 \(<\) 0.9375)AND(A20 \(\geq\) 0.1875 OR A20 \(<\) 0.1719) \\  & AND(A21 \(<\) 0.1875)AND(A23 \(<\) 0.5938) \\  & AND(A26 \(<\) 0.5157)AND(A29 \(<\) 0.4688) \\  & AND(A30 \(<\) 0.75)AND(A31 \(<\) 0.7188 OR A31 \(\geq\) 0.8282)) \\  & THEN B \\ \hline \multirow{7}{*}{Diabetes} & IF((V1 \(<\) 0.25 OR V1 \(\geq\) 0.5)AND(V2 \(<\) 0.8125) \\  & AND(V3 \(\geq\) 0.0313)AND(V4 \(<\) 0.4219) \\ \cline{1-1}  & AND(V8 \(<\) 0.25)) THEN tested\_negative \\ \cline{1-1}  & IF((V1 \(>\) 0.0625)AND(V5 \(<\) 0.2969 OR V5 \(\geq\) 0.625) \\ \cline{1-1}  & AND(V6 \(<\) 0.625)AND(V8 \(\geq\) 0.1094)) THEN tested\_positive \\ \hline \multirow{7}{*}{Parkinson’s} & IF((V1 \(<\) 0.5625)AND(V2 \(\geq\) 0.375) \\ \cline{1-1}  & AND(V3 \(<\) 0.375 OR V3 \(\geq\) 0.875)AND(V16 \(\geq\) 0.3125) \\ \cline{1-1}  & AND(V20 \(<\) 0.4063)) THEN healthy \\ \cline{1-1}  & IF((V1 \(<\) 0.6875 OR V1 \(\geq\) 0.7969) \\ \cline{1-1}  & AND(V18 \(\geq\) 0.4375)) THEN PD \\ \hline \multirow{7}{*}{Mammography} & IF((V2 \(<\) 0.7032 OR V2 \(\geq\) 0.8438)AND(V3 \(<\) 0.9063) \\ \cline{1-1}  & AND(V5 \(\geq\) 0.1875)) THEN benign \\ \cline{1-1}  & IF((V3 \(\geq\) 0.8282)AND(V4 \(<\) 0.0782 OR V4 \(\geq\) 0.4844) \\ \cline{1-1}  & AND(V5 \(<\) 0.9219)) THEN malignant \\ \hline \multirow{7}{*}{COVID-19} & IF (A3 \(\geq\) 0.25 AND A5 \(\geq\) 0.5313) THEN 1 \\ \cline{1-1}  & IF (A3 \(<\) 0.3594 AND A5 \(<\) 0.7813 \\ \cline{1-1}  & AND A6 \(<\) 0.4375) THEN 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: BIN-NLCEE rule set. select the best attributes to compose the rule by using an attribute evaluation method for high-dimensionality datasets. Conceptualization, L.R.d.A. and M.d.S.G. ; methodology, A.H.d.S.A. ; software, A.H.d.S.A. and G.A.C.N. ; validation, M.d.S.G. and L.D.S. ; formal analysis, P.L.L.B. and L.R.d.A. ; investigation, A.H.d.S.A. ; resources, A.H.d.S.A. and G.A.C.N. ; data curation, A.H.d.S.A. and G.A.C.N. ; writing--original draft preparation, A.H.d.S.A. and G.A.C.N. ; writing--review and editing, L.R.d.A., L.D.S., P.L.L.B. and M.d.S.G. ; visualization, L.R.d.A., L.D.S., P.L.L.B. and M.d.S.G. ; supervision, L.R.d.A. ; project administration, L.R.d.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.