A Comparative Study of Random Forest and Naive Bayes Algorithms for Heart Disease Prediction

Approved

Relevance and Originality:

The research addresses a significant health concern, highlighting the increasing prevalence of heart attack cases and the urgent need for early identification systems. The proposal for a portable and reliable application to predict the risk of cardiac ailments is both relevant and original, given the limitations of traditional diagnostic methods like ECG. The focus on fundamental symptoms such as age, sex, and chest discomfort enhances the study's applicability to a broad population.

Methodology:

The methodology is appropriate for the proposed research, particularly the selection of machine learning algorithms (Random Forest and Naïve Bayes) for comparison. However, it would benefit from a clearer explanation of how data will be collected, processed, and analyzed. Detailing the dataset, the criteria for symptom selection, and how the algorithms will be trained and validated would strengthen the methodology.

Validity & Reliability:

The validity of the proposed application relies on the robustness of the machine learning models and the accuracy of the input data. To enhance reliability, it is crucial to discuss how the models will be tested for performance metrics, such as accuracy, sensitivity, and specificity. Providing information on potential biases in the data or challenges in symptom interpretation would also improve the study's credibility.

Clarity and Structure:

The writing presents a clear objective and rationale for the research, but the structure could be enhanced. Organizing the proposal into distinct sections—such as introduction, problem statement, proposed solution, methodology, and expected outcomes—would facilitate easier comprehension. Including visual aids or flowcharts to illustrate the proposed system could further enhance clarity.

Results and Analysis:

While the proposal outlines the intent to compare two machine learning algorithms, it lacks specific details about expected outcomes or how the results will be analyzed. Including a discussion of how the results will inform the effectiveness of the application and its potential impact on early heart attack detection would provide valuable context. Additionally, presenting hypothetical or preliminary results based on existing studies could offer insights into the feasibility of the proposed system.