Published Paper Details:

Data analysis and exploration are necessary for machine learning in order to identify significant trends and patterns. Everyone on Earth relies on allopathic therapies and medications in the modern era. Medical datasets can benefit from machine learning techniques since they offer a wide range of opportunities for both textual and visual data. Data mining is the key to extracting insight from these voluminous, enigmatic data that are present in medical services. This paper presents an API for prescribing medications to users with a certain ailment, which would also be diagnosed by the framework through the analysis of the user's symptoms using machine learning techniques. Here, we make use of some insightful data pertaining to the mining process to identify the most precise illness that may be associated with symptoms. The condition is easily identifiable by the sufferer. Patients may clearly identify their disease by merely attributing their symptoms, and the program interface indicates what disease the user may be infected with. The framework will show complacency in urgent instances where the patient cannot go to a doctor's office or in circumstances where professionals are available in the area. By considering numerous features in the database, predictive analysis on the illness would be carried out, leading to the recommendation of drugs to the user. The outcomes of the experiment can also be applied to healthcare tools and future research.