This project delves into the intricacies of Polycystic Ovary Syndrome (PCOS), a multifaceted medical condition impacting women's health. PCOS affects menstrual cycles, hormone levels, and fertility, presenting diagnostic challenges. The objective here is to analyze a comprehensive dataset to gain insights into PCOS diagnosis and its associated factors.
The dataset used in this analysis was sourced from various healthcare institutions across Kerala, India. Access to and contribution toward enriching this dataset is available on Kaggle.
A critical initial step involved meticulous examination of data integrity. Ensuring the reliability and accuracy of the dataset is essential for subsequent analyses.
Visual representations were employed to elucidate the distribution of PCOS diagnoses within the dataset. Visualizing the data aids in identifying trends and patterns.
Exploring the relationship between diagnostic features and the likelihood of PCOS involved in-depth analysis. Visualizations were instrumental in uncovering correlations and significant factors associated with PCOS.
Comparing feature distributions between PCOS-positive and negative cases facilitated the identification of distinct patterns. These patterns offer valuable insights potentially aiding in more accurate diagnosis and management of PCOS.
This project aims to enhance understanding of PCOS and contribute to advancements in its diagnosis and treatment through comprehensive data exploration.