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

BackgroundPrevious 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.MethodsIn 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 findingsOur 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.ConclusionsWe 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.     

Enhanced succinic acid production under acidic conditions by introduction of glutamate decarboxylase system in E. coli AFP111

Bioprocess and Biosystems Engineering - Biological synthesis of succinic acid at low pH values was favored since it not only decreased investment cost but also simplified downstream purification...     

Winter foraging patterns and voluntary hypothermia in the social caterpillar Eucheira socialis

1. Analysis of 28 years of weather data for the Sierra Madre Occidentals of Mexico showed that while flight, mating, and oviposition of the social caterpillar Eucheira socialis (Lepidoptera: Pieridae) occurred in the warmest and wettest months, much of the caterpillar’s feeding and growth occurred in the winter when nocturnal temperatures often fell below 0 °C. 2. Although daytime temperatures at the study site in midwinter were markedly warmer than overnight temperatures, colonies remained sequestered in their bolsas by day. Caterpillars initiated activity shortly after the onset of darkness and foraged overnight at temperatures as low as ? 2 °C. The remarkably low chill‐coma temperature recorded for this species has been reported previously only for a sub‐Antarctic caterpillar. 3. Temperature measurements on sunny days showed the interiors of bolsas to be thermally heterogeneous, with an average differential of 12 °C between the warmest and coolest regions of the structure. Although caterpillars clustered within the bolsas had body temperatures significantly greater than ambient, they exhibited voluntary hypothermia by day, seeking out and resting in the coolest pockets of the bolsas. 4. Voluntary hypothermia may influence growth rate adaptively and prevent acclimatisation to daytime temperatures that would have a negative effect on the caterpillar’s ability to locomote at low overnight temperatures.