PREDICTING BREAST CANCER AND DIABETES PRECISION OF MACHINE LEARNING TECHNIQUES USING DATA CLEANING AND VISUALIZATION TECHNIQUES

P. Nisha Priya B.E. M.E; Sandhiya. K

PREDICTING BREAST CANCER AND DIABETES PRECISION OF MACHINE LEARNING TECHNIQUES USING DATA CLEANING AND VISUALIZATION TECHNIQUES

Authors: P. Nisha Priya B.E. M.E, Sandhiya. K

Unique Paper ID: 173421
Volume: 11
Issue: 10
PageNo: 325-336

Keywords: No Keywords Found

Abstract:
In the era of advanced technology, the integration of machine learning techniques in healthcare has shown significant promise in predicting and preventing various health conditions. This project focuses on the development of a Smart Health Prediction System using machine learning algorithms to predict three critical health issues: diabetes, breast cancer. The primary objective is to leverage the power of predictive analytics to assist healthcare professionals in early diagnosis and intervention, thereby improving patient outcomes. The project employs a Python-based machine learning framework, utilizing popular libraries such as scikit-learn, TensorFlow, and Keras. For breast cancer prediction, the project will use a dataset featuring characteristics derived from various medical inputs of breast tissue. Machine learning models will be implemented to analyze these inputs and predict the presence of malignant tumors. The proposed Smart Health Prediction System aims to provide accurate and timely predictions, enabling healthcare professionals to prioritize high-risk individuals for further diagnostic assessments. The integration of machine learning in health prediction not only facilitates proactive healthcare but also contributes to a more personalized and efficient patient care paradigm. The increasing prevalence of breast cancer and diabetes has prompted the need for efficient and accurate predictive models to aid in early diagnosis and treatment. Machine learning (ML) techniques offer a promising approach for predicting these diseases by analyzing large datasets. However, the quality of the data used in these models significantly influences their performance. This study focuses on improving the precision of ML models for predicting breast cancer and diabetes by employing advanced data cleaning and visualization techniques. Data cleaning is essential for removing inconsistencies, missing values, andoutliers that could adversely affect the learning process. Visualization techniques are used to better understand the relationships between variables, identify patterns, and make data-driven decisions about the preprocessing steps. Several ML algorithms, including decision trees, support vector machines, and logistic regression, are applied to the cleaned datasets, and their performance is evaluated in terms of accuracy, precision, recall, and F1 score. The results show that careful data cleaning and visualization lead to significant improvements in the prediction accuracy of breast cancer and diabetes models.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 325-336

PREDICTING BREAST CANCER AND DIABETES PRECISION OF MACHINE LEARNING TECHNIQUES USING DATA CLEANING AND VISUALIZATION TECHNIQUES

Available:https://ijirt.org/Article?manuscript=173421

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