Project Paper Name - “Machine Learning Based Approaches for Cancer Prediction”

ABSTRACT :

The rapid expansion of data and databases has necessitated the development of new tools and procedures that can convert raw data into useable information rapidly and effectively. The advancement of information technology has prompted simultaneous growth in database storage and management capabilities in recent years. The need for retrieving the implicit information included in recorded data to improve decision-making In business, healthcare services, research, and other areas grows greater. As a result, obtaining meaningful information from data kept in vast repositories (also known as "knowledge discovery") is seen as a fundamental need in many fields. The subject field is known as "data mining" has been a major issue in databases and Artificial Intelligence (AI). Cancer is one of the most serious problems facing society today, and identifying cancer at an early stage remains a difficult task for specialists. In order to create innovative ways for detecting and preventing cancer, it is critical to first identify and understand genetic and environmental variables. It is for this reason that a revolutionary multi-layered strategy that in corporate clustering and decision tree techniques is used in the development of a cancer risk prediction system. The suggested system accurately predicts lung, breast, oral, cervical, stomach, and blood cancers, and it is both user-friendly and cost-saving in terms of operation and maintenance. To identify possible cancer patients, this study uses data mining methods such as classification, clustering, and prediction to analyze large amounts of data. This cancer prediction method, which is based on data mining techniques, has been suggested by us. This approach predicts the likelihood of developing breast cancer at an early stage. The projected outcomes of this method are compared to the patient's past medical information, which serves as validation. The primary goal of this strategy is to offer consumers an early warning while still being cost effective for the user. Finally, a prediction system is established to examine risk levels, which aids in the development of a prognosis for the patient. This study aids in the discovery of a person's propensity to cancer prior to the administration of clinical and laboratory testing, which are both expensive and time-consuming.

Keywords: Artificial Intelligence(AI), Cancer, Data Mining (DM), Clustering, Classification.

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