DETECTION AND PREDICTION OF INFECTIOUS DISEASES USING IOT AND MACHINE LEARNING
Mohammad Meraj1, Syed Ahad Murtaza Alvi1
1Lecturer,College of Applied Computer Sciences ,King Saud University, Saudi Arabia
Overwhelming diseases have suppressed the world’s relations, and professionals need to spend more energy and discrimination strategies in follow-up treatment. These diseases can activate people’s thinking in life. Early assessment can keep up with clinical knowledge to a large extent, thus saving more lives. The pandemic has led to increased visits to crisis centers, clinical workplaces and clinical think tanks. This statement becomes a meaningful answer to these valuation questions. The use of specially agreed designs and proposed mechanized solutions systems facilitated this assessment to study the most unusual obsessive-compulsive disorder in Iraqi society. Carry out clinical work in various conditions around Baghdad. The first analysis of the collected answers (100 answers) showed that diabetes, influenza and typhoid fever are the most prominent problem-solving problems in Iraqi society. Another subsequent investigation clearly examined the signs and factors of this evil blood test, which correctly pointed out the lack of a satisfactory and vivid assessment of the torture faced by Iraqi society. Illness (flu and typhoid) was used as a component space for one of the AI strategies, the unexpected impact of Sierra. Various measures are used to evaluate the results, such as accuracy, hybrid structure, and ROC adaptability testing to show development progress. The results show that compared with others, typhoid fever has a basic estimation accuracy of 96%, and three AI structures are help to study influenza infection. In the combination of the three models, this is 93%, showing extraordinary performance. re shows meticulous and precise methods for separating big problems.
Keywords. Infectious diseases, IOT, KNN, Diabetes, Symptoms, Machine learning