Presentation of futuristic Malarial Disease through a Hybrid Model of A.I. and Big data
Abhishek Kumar Awasthi, Minakshi Sharma, Arun Kumar Garo, Prayanshu Chaudhary · 2023
Transmission of parasites of female Anopheles mosquito is a major cause for the fatal disease like Malaria and most common symptoms are high fever, headache, abdominal pain, muscle pain, vomiting, diarrhea, Anemia, etc.Healthcare is a field where Forecasting is most beneficial and helpful for the cure of diseases and it can be possible only by using models of Neural Networks, Regression, and LSTM for predicting that how many confirmed cases will occur, how many people will get recovered and among them how many get dead cause of any disease based on past and present data for forecasting the future trend of these cases. Even though forecasting can be done by traditional methods but traditional methods are time consuming. The machine learning techniques are fast and accurate then also for adding efficiency and accuracy to these methods and techniques, Artificial Intelligence takes place in the field of Data Sciences. Artificial Intelligence came into the picture 100 years ago and shows remarkable growth in every field in the past few years. There are a lot of models proposed for different types of diseases in the field of healthcare. The visible patterns of symptoms and cases related to the disease can help in the medical field to prevent and cure it. This work is going to deal with the applications of Artificial Intelligence in Data Sciences which is to make an early reliable prediction of Malarial disease using Neural Networks, Regression, and LSTM model. It Compares the trends of different models and comparative results will demonstrate that the machine learning techniques practiced to forecast the malarial disease along with visible patterns gives information related to the patient’s data.