A Machine Learning Model for the Prediction of Sexually Transmitted Diseases among the Youths in Southwestern Nigeria

Sexually transmitted diseases (STDs) are diseases which are spread between individuals through unprotected sexual contact. The spread has become rampant, especially among the youths nowadays who display promiscuous characteristics, which leads to a faster rate of the spread of the disease among the youths. Thus, this study aims to develop a machine learning model for an accurate analysis and prediction of the transmission rate of STDs among the youth within the southwestern region of Nigeria. For an approximate and optimize study, a questionnaire in Google form was administered to harvest opinions of youths within the stated demographic with respect to their health status, disease awareness, lifestyle choices and other characteristics. The collected primary dataset of 529 individual responses was used to build the machine learning model. The dataset was converted to comma-separated values (CSV) format to be trained and tested for a well-supervised machine learning model. Of the data collected, 75% served as training data and 25% served as testing data. Feature extraction, data visualization and data preprocessing were done to convert raw data into suitable machine learning. Taking from the results, a decision tree accuracy of 0.9393 with an area under the curve (AUC) score of 0.5843, logistic regression accuracy of 0.9621 with AUC score of 0.5, support vector machine accuracy of 0.96212 with AUC score of 0.5 and Gaussian Naïve Bayers machine learning algorithms accuracy score of 0.5909 with AUC score of 0.7874 were obtained. Hence, the Gaussian Naïve Bayers gave the best outcomes with an area under the curve (AUC) score of 0.79 and was able to correctly classify all 5 cases of STDs within the test set as compared to other algorithms.