Assessing aflatoxin safety awareness among grain and cereal sellers in greater Accra region of Ghana: A machine learning approach

Studies have established high prevalence of aflatoxin contamination in grains and cereals produced Ghana. Mitigation strategies focused mainly on capacity building for farmers, agricultural extension officers, bulk distributors processors to the detriment market women who act as final link between consumers producers. This study used supervised machine learning algorithms by means Classification Regression Trees (CART) investigate knowledge awareness Greater Accra Region A cross-sectional survey probability sampling methods were employed data collection. Ninety-two (92%) participants had never heard about aflatoxins yet, 62% reported that they usually observe mould growth their cereals/grains. Unsurprisingly, 97% indicated no bill passed government Ghana parliament. Despite not being aware menace, percent correctness safety measure score was 40%. regression tree algorithm showed that, participant's ethnic group most significant parameter consider regarding knowledge. Their educational background age 95.5% 72.5% group. classification level when it comes sorting grains/cereals. marital status 92.4% 89.3% important level. It is therefore imperative extend sensitization programs these women, targeting uneducated specific groups.