Implementing a Smart Low-Cost System for Diagnosing Bacteria in Women

A checking test known as the bacteria culture test is required to determine harmful bacteria. This traditional isolation and cultivation technique often used in clinical microbiology is time-consuming and laborious. Thus, it is essential to implement new smart, efficient, and cost-effective tools for accurate and rapid identification of bacteria. This work developed a smart low-cost system for diagnosing five types of bacteria in a woman's vagina. The crucial components of the proposed system comprise a five-sensor array, a controller (Arduino board), and a computer (laptop). Furthermore, the system software includes data saving, plotting code, and an artificial neural network for diagnosing bacteria. Three commonly used classifiers were used for comparative purposes: a Support Vector Machine, Random Forest, and Logistic Regression. The system with the Random Forest classifier achieved the highest scores in accuracy, precision, and F1 score, while the Logistic Regression classifier performed the worst. The proposed system added a clinically smart and helpful tool for the diagnosis of bacteria systems and the decision-making process.