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

Abstract

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