Shilpa Yadav - Academia.edu (original) (raw)
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Papers by Shilpa Yadav
IJRAR, 2023
Data mining can extract essential information from unstructured data. With the continuous growth ... more Data mining can extract essential information from unstructured data. With the continuous growth and expansion of the healthcare sector, the need for creating successful decision support systems for medical applications is becoming increasingly important. This article offers a comparative examination of different machine learning (ML) models to forecast multiple illnesses. The investigation employs a dataset containing health records of patients, encompassing demographic, clinical, and laboratory factors, to assess the effectiveness of distinct ML models. Prompt decision-making is crucial in the diagnosis of diseases. This paper examines a study comparing various machine learning models for forecasting diseases using different attributes and a more effective algorithm. The research offers recommendations that could greatly benefit the healthcare industry, and the project's actualization provides practical insights. The paper concludes with a summary of the authors' contributions to the topic, addressing any shortcomings and suggesting areas for future improvement. This study will be useful for physicians and researchers in identifying critical traits for disease identification. The study results can help with the selection of suitable ML models for predicting diseases and offer valuable insights into the advantages and disadvantages of various models.
IJRAR, 2023
Data mining can extract essential information from unstructured data. With the continuous growth ... more Data mining can extract essential information from unstructured data. With the continuous growth and expansion of the healthcare sector, the need for creating successful decision support systems for medical applications is becoming increasingly important. This article offers a comparative examination of different machine learning (ML) models to forecast multiple illnesses. The investigation employs a dataset containing health records of patients, encompassing demographic, clinical, and laboratory factors, to assess the effectiveness of distinct ML models. Prompt decision-making is crucial in the diagnosis of diseases. This paper examines a study comparing various machine learning models for forecasting diseases using different attributes and a more effective algorithm. The research offers recommendations that could greatly benefit the healthcare industry, and the project's actualization provides practical insights. The paper concludes with a summary of the authors' contributions to the topic, addressing any shortcomings and suggesting areas for future improvement. This study will be useful for physicians and researchers in identifying critical traits for disease identification. The study results can help with the selection of suitable ML models for predicting diseases and offer valuable insights into the advantages and disadvantages of various models.