Development and Analysis of Machine Learning Models for Healthcare and Agriculture Applications

Abstract

newline newlineIn a time when data is used to inform decisions in many different industries, integrating newlinemachine learning models offers a potent chance to solve urgent real-world newlineissues. The creation and implementation of a framework for machine learning newlineintended to address the significant problems in healthcare and agriculture are examined newlinein this thesis. In particular, the research aims to estimate the incidence of newlinecovid-19 cases in India, detection of heart failure, crop yield prediction, analysis newlineof crop production in Uttar Pradesh to optimize crop output, diabetes diseases newlineprediction, and groundwater level prediction in India. newlineThe study thoroughly analyzes the literature to find possibilities and gaps in newlinetoday s approaches. Using this framework as a starting point, the thesis describes newlinecreating a machine-learning models by using different kinds of machine learning newlinealgorithms that uses massive datasets to produce precise forecasts and useful insights. newlineThrough the analysis of variables like weather patterns, crop management newlinetechniques, and soil health, the model seeks to improve crop yields in agriculture. newlineIn terms of healthcare, the emphasis is on covid-19 cases, diabetes, and heart newlinedisease early and accurate diagnosis, which is essential for prompt intervention newlineand treatment. newlineThe outcomes illustrate the model s ability to significantly advance both domains newlineby demonstrating its efficacy in producing dependable forecasts. This study newlinehighlights the revolutionary effect of machine learning towards resolving real-world newlineissues by converting complicated data into workable solutions. newlinev newlineThe ultimate goal of this thesis is to close the knowledge gap between theoretical newlineand practical machine learning applications, providing a means of enhancing newlineagricultural output and improving health outcomes. The work adds to the expanding newlinevolume of information in machine learning as well as its applications newlinein resolving important societal issues using rigorous analysis and creative model newlinecreation. newline