PESTICIDE POISONING DIAGNOSIS SYSTEM

Pesticide poisoning has become a growing health concern in many agricultural regions where farmers and workers frequently interact with chemical pesticides. Improper pesticide handling, accidental exposure, and unsafe spraying practices can lead to serious health complications and risk to human life. Traditional diagnosis methods such as manual symptom observation or laboratory testing often fail to provide timely identification of poisoning cases. This paper presents a machine learning– based Pesticide Poisoning Diagnosis System designed to identify possible poisoning cases using symptom analysis and medical data processing techniques. The proposed system utilizes classification algorithms to analyze patient symptoms and exposure history with high accuracy and fast processing capability. Once a potential poisoning case is identified, the system generates a diagnosis report and provides supportive medical recommendations. The diagnosis information can then be used by healthcare professionals to enable quick response and preventive medical treatment.