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A Noninvasive model to detect Dengue based on symptoms using Artificial Intelligence and Machine Learning

Author(s): Ruban S, Naresha, Sanjeev Rai


Artificial Intelligence has been transforming various sectors ranging from Finance, Entertainment, sports, Healthcare etc. The role of AI in healthcare will have an impact on all our lives owing to the change it brings to the Patientcare system, changing the traditional way of handling illness and diseases. Most of the AI based applications use Machine Learning algorithms that use data. Hence the source of Data and the nature of Data holds the key to developing effective AI based solutions for many health issues in society. However, Data has been available in all the hospitals and medical care facilities for many years now. They cannot be used directly to develop AI based applications until and unless they are transformed and made into a format in which machine learning algorithms can work. In this research paper, we discuss the process of developing an AI based application to predict Dengue, one of the vector borne diseases, based on the symptoms. Our work was done on the data collected from the clinical notes of a 1500-bed hospital in the coastal district of Karnataka. We have implemented a few of the machine algorithms like Logistic regression, Support vector machine and Decision Tree classifier. As the dataset is highly imbalanced (1:50), we applied over-sampling techniques (Random over sampling, SMOTE) to overcome this problem. We compared the over-sampling techniques and find that combination of SMOTE and Decision Tree classifier gave the best result (98% F1 micro score) compared to the other algorithms that we used in this study.

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