Exploratory Data Analysis for Detecting of Iron Deficiency Using Machine Learning Techniques

Iron deficiency, a prevalent issue in global health, impacts a large number of individuals and can result in symptoms such as weariness, difficulty breathing, and other incapacitating effects. Although blood tests are considered the most reliable method for diagnosis, they can be inconvenient and intrusive. This dissertation explores the promising possibilities of machine learning in providing a non-invasive method for detecting iron insufficiency.Anemia caused by insufficient iron levels is a widespread global health issue. Conventional techniques for detection sometimes entail intrusive procedures such as blood testing. This dissertation investigates the capacity of machine learning methods to identify iron deficiency without the need for intrusive procedures.The research will conduct a thorough examination of numerous data sources that may be associated with iron insufficiency using Exploratory Data Analysis (EDA). This may include physiological data such as heart rate and oxygen saturation, in addition to any accessible blood test findings. In addition, the inquiry will examine the potential use of image analysis from easily accessible places such as fingernails, palm, or the conjunctiva of the eye.The dissertation will utilise the knowledge obtained from exploratory data analysis (EDA) to assess and evaluate the effectiveness of several machine learning algorithms in detecting iron deficiency. This comparison research aims to identify the best precise and user-friendly method for non-invasive detection.Multiple machine learning methods are being examined to determine their efficacy in detecting iron insufficiency. The dissertation evaluates the efficacy of different algorithms and determines the best viable method for precise and non-intrusive diagnosis of iron insufficiency. This study has the capacity to transform the process of identifying iron insufficiency. The findings might potentially lead to early diagnosis and therapies, which would improve patient health outcomes. This would be achieved by establishing a system that is cost-effective and accessible to a large number of people.