Transparent Peer Review By Scholar9

Detection of Kidney Disease using Machine Learning & Data Science

Abstract

Kidney disease identification with machine learning and data science is transforming patient consideration and early diagnosis by using predictive models to identify important risk factors and biomarkers. There are several organs in the human body that performed vital functions. The kidney is a vital organ that removes toxic substances from the body, filtering blood. The reason for this is that the kidney is considered to be one of the important body parts. To maintain the health of the body, the kidneys should be safeguarded. Which kidney is affected by a different illness depends on a number of factors. The reason behind renal illness appears to be different in different individuals. The renal disease dataset (obtained via Kaggle) has been subjected to machine learning in this investigation to identify indicators of kidney illness. The primary goal of the data study has been to identify the core sources of the data, which has allowed for the distinction of any negative consequences. To choose the fundamental attributes of the data in this case, the connection component has been used. The data has been concluded using those foundational credits, and the implications of machine learning classifiers have begun kidney disease diagnosis.

Srinivasulu Harshavardhan Kendyala Reviewer

Review Request Accepted

Srinivasulu Harshavardhan Kendyala Reviewer
16 Oct 2024 03:17 PM

Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

Relevance and Originality:

This research addresses a critical and relevant issue in healthcare by leveraging machine learning and data science for the early identification of kidney disease. The originality of the study lies in its application of predictive models to uncover significant risk factors and biomarkers, contributing to improved patient care and diagnosis. Given the rising incidence of kidney-related disorders globally, the relevance of this work is underscored by its potential to transform how kidney diseases are diagnosed and managed. By utilizing a dataset obtained from Kaggle, the study reflects a modern approach to data analysis in medical research.

Methodology:

The methodology employed in this research appears systematic, utilizing a renal disease dataset for machine learning analysis. The focus on identifying core sources and attributes of the data indicates a thorough data exploration process. However, further details regarding the specific machine learning techniques used, such as the algorithms applied and the rationale behind their selection, would enhance clarity. Additionally, the description of how the connection component was utilized to select fundamental attributes could be elaborated upon to provide a clearer understanding of the analytical process.

Validity & Reliability:

The validity of the findings is contingent on the quality and representativeness of the dataset. Utilizing a widely recognized dataset like that from Kaggle lends credibility to the research. However, the paper could strengthen its reliability by discussing potential limitations or biases in the dataset, as well as the methods used to validate the machine learning models, such as cross-validation or performance metrics like accuracy, precision, and recall. Including a comparison with existing diagnostic methods would also provide a benchmark for assessing the effectiveness of the proposed machine learning approach.

Clarity and Structure:

The paper's structure could benefit from a clearer organization to guide readers through the research process. While the introduction highlights the significance of kidney disease identification, a more explicit breakdown of the methodology, results, and discussion sections would improve readability. Clear headings and subheadings can aid in navigating the content. Additionally, summarizing key findings and implications in bullet points or tables would enhance clarity and allow readers to quickly grasp essential information.

Result Analysis:

The analysis of results should include a more detailed examination of the machine learning classifiers used in the study. Presenting specific performance metrics and discussing how different classifiers compare in terms of effectiveness for kidney disease diagnosis would provide valuable insights. Additionally, exploring the practical implications of the findings for healthcare practitioners, such as how these predictive models can be integrated into clinical settings, would enhance the research's relevance. Including recommendations for future research or potential improvements to the methodology could also inspire further exploration in this critical area of study.