Display omitted •Non-conventional feature engineering reduces data complexity into two features.•Selected features present high interpretability with the biosensing mechanism.•Machine learning method for electrochemical biosensors via cyclic voltammetry.•ML model deployed enhances reliability of the virus biosensing. Electrochemical biosensors are small analytical devices that convert a biological response into a processable signal with high sensitivity, ease of operation, cost-effectiveness, and miniaturization capability. The current study proposes enhancing the reliability of the electrochemical detection of enveloped viruses, such as the rabies virus using feature engineering and machine learning. Portable detection was achieved using a graphene-based microfluidic sensor and the current was recorded from electrochemical experiments using a portable potentiostat. After data mining and feature engineering, a dataset obtained from staircase cyclic voltammetry was used in different machine learning models. The features from the voltammogram were extracted following theoretic aspects to build a dataset with uncovering patterns and valuable information. Correlation-based and recursive feature elimination algorithms were used for feature engineering. Our analysis showed that the best F-measure score a model trained was obtained through a support vector machine with 0.9830 for the diagnostic tasks following the proposed feature engineering pipeline. On the other hand, the preprocessing pipeline which is commonly used in many studies obtained a score of 0.9394 using a decision tree model. In addition, the proposed method generates features with higher interpretability and lower model complexity than conventional feature reduction methods such as PCA. Our study shows the potential of machine learning models in the field of electrochemical biosensor development with a focus on feature engineering based on electroanalytical analysis, which can be extended to develop new tools for disease diagnosis in the future.