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@article{169197,
        author = {Remalli Rohan and J Chaitanya and Anthappagudem Samatha and V. Shirisha},
        title = {Enhancing PID Mellitus Classification through Decision Trees and Random Forest Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {779-784},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169197},
        abstract = {Diabetes mellitus, or just diabetes, is becoming more widespread. Diabetes presents considerable hurdles in prognosis because it cannot be cured but can be efficiently controlled with early detection. Manual assessments for early detection might be problematic since they rely on healthcare professionals' observations, which may miss important trends. As a result, automated computer-based analysis is critical for the early detection of Pima Indian Diabetes (PID). This study seeks to provide an automated system for detecting and classifying PID. The dataset, supplied by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), is intended to predict diabetes risk using a variety of diagnostic indicators. The dataset specifically contains Pima Indian women aged 21 and up, drawn from a larger population. After gathering the dataset, it was pre-processed to identify and remove null values. Next, exploratory data analysis (EDA) was used to investigate the correlations between the attributes. Feature selection was then performed to prepare the data for machine learning classifiers. Finally, eight input variables were found, with one target variable employed by twelve machine learning methods. Among these, the Decision Tree (DT) and Random Forest (RF) classifiers were the best in detecting PID diabetes. This study emphasises the importance of automated procedures in diabetes diagnosis and management, as they provide a vital tool for early intervention.},
        keywords = {Diabetes, Decision tree classifier, EDA, Pima India Dataset, Random Forest classifier.},
        month = {November},
        }