Author(s): Divya, Tanvi Dhingra, Rajat Nagar, Pawan Kant Tiwari and Deepika Singh

Abstract: The design and implementation of various well-known data mining techniques in a variety of real-world applications (e.g., industry, healthcare, and bioscience) has led to their use in machine learning environments to extract meaningful information from provided data in healthcare communities, biological disciplines, and other fields. Early illness prediction, patient treatment, and community services all benefit from precise medical database analysis. Machine learning techniques have been effectively used in a variety of applications, including disease prediction. The goal of developing a classifier system utilising machine learning algorithms is to greatly assist physicians in predicting and diagnosing diseases at an early stage, which will greatly aid in solving health-related difficulties. For our study, a sample of 4920 patient records diagnosed with 41 disorders was chosen. We chose 95 out of 132 independent variables (symptoms) that are strongly associated to illnesses and improved them. The disease prediction system built utilising Machine learning techniques such as Decision Tree classifier, Random forest classifier, and Nave Bayes classifier is demonstrated in this research paper. This paper “Disease Prediction Using Django and Machine Learning” gives a comparison of the outcomes of the aforementioned algorithms.

DOI: 10.33545/26633582.2022.v4.i1a.67

Pages: 40-48 | Views: 1047 | Downloads: 726

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