Development of Noble Method of Medical Diagnosis using Machine Learning Algorithms

Abstract

Machine Learning which is also known as ML, a subset of Artificial Intelligence or called (AI), has been successfully used in the industry of healthcare to diagnose disorders. ML methods can diagnose both common and unusual diseases. However, the accuracy of machine learning in disease diagnosis remains an issue. The performance of different ML techniques varies depending on the healthcare dataset used. As a result, it is critical to apply numerous cutting-edge algorithms with excellent code efficiency in order to streamline the search for the best machine learning method for diagnosing a certain condition.ML is an autonomous system that learns by itself that has evolved from the use of explicitly coded data to deep analysis. One of the primary goal of the Machine learning system is to enable the machine to streamline the process and complete the task without the help of any assistance. It incorporates and involves humanenvolvment within the analytic system.ML algorithms are divided into two categories: One is supervised and another is unsupervised. A supervised learning system is one in which the instances of the concerned data forecast its future data, however unsupervised learning is a procedure in which the data is taught, classified, or labeled.In our research, we show that various libraries can be used for the comparison of the performance of several machine learning algorithms for detecting a disease using a particular dataset, all with a few lines of code. Medical diagnostics are performed with the goal of predicting illness identification with high accuracy and respecting each part of the human biological system. The identification of Cancer cells, heartbeat analysis, disease identification structure, evaluation of nervous system, ortho-care system, various disease identification analyses, and so on represent a few of the medical diagnosis applications. The applications of this kind of diagnostic system can provide the appropriate answer for speedy disease recovery.

Introduction

Machine Learning (ML), a key subset of Artificial Intelligence (AI), uses algorithms to learn from data and improve performance over time. In healthcare, ML has been used since the 1970s to diagnose diseases and now plays a crucial role in early diagnosis, health monitoring, and treatment recommendations.

Key Highlights:

I. Machine Learning in Healthcare:

• Applied in disease diagnosis, especially chronic conditions like diabetes, cancer, and heart disease.

• Helps reduce diagnosis time and cost, overcoming limitations of traditional methods.

• Algorithms such as SVM, Naïve Bayes, Random Forest, KNN, and Logistic Regression are commonly used.

• Supervised learning is most prevalent in medical diagnostics.

II. Problem Statement:

• Diagnosis remains a challenge due to complex and multivariable patient data.

• Choosing the most effective ML algorithm for specific healthcare datasets is critical.

• Early detection is essential for improving treatment outcomes and reducing errors.

III. Data Preparation & Methodology:

• Data Collection: From sources like CDC, WHO, UCI ML Repository, Kaggle, MIMIC-III.

• Data Types: Structured (numerical/categorical), unstructured (X-rays, ECGs).

• Preprocessing: Includes handling missing values, removing outliers, normalization, and standardization.

• Feature Selection: Uses techniques like PCA, Chi-Square, and Random Forest feature importance.

• Data Splitting: Divided into training (70%), validation (15%), and testing (15%).

IV. Model Training & Selection:

• Various algorithms are applied and evaluated for accuracy and performance:

  • Decision Trees (DT)

  • Support Vector Machines (SVM)

  • K-Nearest Neighbor (KNN)

  • Naive Bayes (NB)

  • Logistic Regression (LR)

  • Artificial Neural Networks (ANN)

V. Evaluation Metrics:

• Accuracy: Correct predictions / total predictions.

• Precision: True positives / (True positives + False positives).

• Recall: True positives / (True positives + False negatives).

• F1 Score: Harmonic mean of precision and recall.

• Confusion Matrix: Shows true/false positives and negatives.

Heart Disease Prediction Use Case:

• ML models use patient demographics, symptoms, lifestyle, and history to predict heart disease.

• Algorithms like KNN, Random Forest, SVM, and Decision Trees are implemented.

• Predictions are validated using performance metrics to ensure clinical reliability.

Conclusion

This Medical diagonsis system\'s primary objective is to forecast the illness based on its symptoms. This system generates a disease prognosis as its final output after receiving the symptoms of the client as input. This yields an average accurate forecast probability of 100%. The Grails framework was successfully used to build the Disease Predictor. This system is easy to use and offers a user-friendly environment. Anyone can access the system at any time and from any location. Lastly, in illness risk modeling, the variety of hospital data determines how good the risk prediction is. The use of machine learning (ML) algorithms for medical diagnosis has shown great promise for improving disease identification and prognosis. While classic models like DT, SVM, and NB continue functioning well, models using deep learning show great promise for medical imaging and difficult data analysis. ML-based diagnostic tools can improve early disease detection greatly when used in an organized manner that includes data preparation, feature selection, model training, and evaluation. The continued development of artificial intelligence (AI) healthcare solutions will alter the diagnostic procedures, making them simpler, more accurate, as well as available to a larger audience.

References

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Copyright

Copyright © 2025 Ambika Goyal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.