Unlocking the Anticancer Potential of Acai Berries using Genetic Insights & Gradient Boosting

The escalating incidence of cancer necessitates innovative, cost-effective therapeutic alternatives. The therapeutic potential of acai berries an agricultural product as a bioactive natural remedy for oncological applications with a specific focus on the chemo-preventive and chemotherapeutic effects against colon, bladder, and breast carcinomas. However, in India, the cultivation and utilization of acai berries are constrained by inadequate agricultural practices and technological adoption. The existing system fails to address the potential of machine learning in agricultural optimization and health research, paving the way for future advancements in data-driven agriculture and healthcare applications. Hence the proposed Gradient Boosting model evaluates the feasibility of cultivating acai berries, in India, market price, and cancer prevention findings based on advanced agronomic techniques. These predictive analytics and precision agriculture methodologies provide valuable insights for farmers and researchers. The proposed work is carried out in three phases: Acai berry yield, Market Price and the cancer prevention study findings. The Gradient Boosting Regressor was used to predict the yield and market price, while the Gradient Boosting Classifier was applied to determine the cancer prevention study findings. The models were trained and evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R²), and classification report metrics. Finally the performance evaluation demonstrated the models’ ability to provide accurate predictions, offering valuable insights into acai berry cultivation and its potential in cancer prevention. The predictive function developed allows users to input various agricultural parameters and obtain predictions for yield, market price, and cancer prevention study findings, facilitating informed decision-making.