Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data

Abstract

Background Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors. Methods In this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children’s parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections. Key findings Our study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics. Conclusions We demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children’s parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.

Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach

“…It is a fundamental data mining technique that thoroughly searches for frequent patterns, correlations, and associations among the sets of variables making them ideal for discovering predictive rules from medical data repositories [ 60 , 61 ]. It has been used in prior healthcare research to identify risk factors for various health outcomes such as early childhood caries [ 62 ], parasite infection [ 63 ], motorcycle crash casualty [ 64 ], stroke [ 65 ], and to discover symptom patterns of coronavirus disease of 2019 (COVID-19) [ 66 ].…”

Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data

“…The training split was used to generate the learned models, while the testing dataset was used for the validation phase to assess the performance of each model to predict the class labels (answering yes or no to TM try). One-hot encoding was applied to all categorical variables with more than two categories, with missing data considered as a category and the elimination of one category for each factor to avoid multicollinearity [ 7 ]. Collinear covariates, with a variance inflation factor > 2.5, were excluded from the analysis [ 8 ].…”

“…More specifically, each of the models underwent a tenfold cross-validation and the classifier hyperparameters were tuned. A random-search approach for model parameter tuning was used to determine the optimal combination of hyperparameters for maximizing accuracy to generate the best model parameters [ 7 ]. To ensure robust results, the cross-validation was performed ten times using a different random number generator seed each time [ 7 ].…”

Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren

“…These approaches appear to be a promising alternative to logistic regression since they avoid overfitting ( 28 , 29 ). ML techniques have previously been used to identify risk factors for parasitic infections, congestive heart failure, diabetes, overweight/obesity, and dementia ( 30 – 34 ). Moreover, several recent studies have used ML approaches to effectively predict undernutrition outcomes in Bangladesh, India, and Ethiopia ( 35 – 39 ).…”

“…Despite these advances, most studies failed to implement association rule learning, another promising ML method that may facilitate the identification of risk factors. Association rule learning has previously shown promise in predicting disease co-occurrences and risk factors for parasitic infection ( 34 , 40 ). Altogether, these ML methods could identify important risk factors for undernutrition, and potentially provide crucial insights into how the co-occurrence of variables may lead to undernutrition.…”