Multiple Disease Prediction Using Machine Learning Algorithms

Year : 2024 | Volume :14 | Issue : 03 | Page : 35-39
By
vector

Ankit Sharma,

vector

A. N. Kshirsagar,

vector

Anish Kannawar,

vector

Abhishek Mishra,

  1. Student, Department of E&TC, SKNCOE, SPPU, Pune, Maharashtra, India
  2. Assistant Professor, Department of E&TC, SKNCOE, SPPU, Pune, Maharashtra, India
  3. Student, Department of E&TC, SKNCOE, SPPU, Pune, Maharashtra, India
  4. Student, Department of E&TC, SKNCOE, SPPU, Pune, Maharashtra, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_113268’);});Edit Abstract & Keyword

The incorporation of machine learning algorithms into healthcare has transformed disease prediction and diagnosis. This research introduces a method for predicting various diseases using machine learning techniques. A comprehensive dataset, consisting of patient records, medical histories, and key disease-related features, was utilized to build predictive models. Data preprocessing methods, including feature selection and normalization, were implemented to clean and prepare the dataset. Several machine learning algorithms, such as Decision Trees, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN), were applied to train and assess the performance of the models. The primary goal of this initiative is to significantly improve healthcare delivery by offering timely and precise predictions for a range of chronic conditions, including diabetes, cardiovascular diseases, cancer, and respiratory disorders. These predictive models will rely on advanced algorithms and data analysis techniques, which will process patient information to generate real-time insights. These insights will then be integrated into a user-friendly, digital platform designed specifically for healthcare professionals. This platform aims to streamline diagnosis and treatment planning, enabling more personalized, proactive care that can lead to better patient outcomes and more efficient healthcare management.

Keywords: Machine learning, disease prediction, predictive modeling, decision trees, random forest, support vector machines, k-nearest neighbors

[This article belongs to Research & Reviews : A Journal of Immunology (rrjoi)]

How to cite this article:
Ankit Sharma, A. N. Kshirsagar, Anish Kannawar, Abhishek Mishra. Multiple Disease Prediction Using Machine Learning Algorithms. Research & Reviews : A Journal of Immunology. 2024; 14(03):35-39.
How to cite this URL:
Ankit Sharma, A. N. Kshirsagar, Anish Kannawar, Abhishek Mishra. Multiple Disease Prediction Using Machine Learning Algorithms. Research & Reviews : A Journal of Immunology. 2024; 14(03):35-39. Available from: https://journals.stmjournals.com/rrjoi/article=2024/view=0

Full Text PDF

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_113268’);});Edit

  1. Kazemi Y, Mirroshandel SA. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med. 2018; 84:117–26.
  2. Barakat H, Andrew P, Bradley H, Mohammed Nabil Barakat H. Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed. 2009;14(4):1–7.
  3. Patil RT, Sherekar SS. Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int J Comput Sci Appl. 2013;6(2):256–61.
  4. Princy RJP, Parthasarathy S, Hency Jose PS, Lakshminarayanan AR, Jeganathan S. Prediction of cardiac disease using supervised machine learning algorithms. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS); 2020. p. 570–5. doi: 10.1109/ICICCS48265.2020.9121169.
  5. Deepika P, Sasikala S. Enhanced model for prediction and classification of cardiovascular disease using decision tree with particle swarm optimization. In: 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA); 2020. p. 1068–72. doi: 10.1109/ICECA49313.2020.9297398.
  6. Grampurohit S, Sagarnal C. Disease prediction using machine learning algorithms. In: 2020 Int Conf Emerg Technol (INCET); 2020. p. 1–7. doi: 10.1109/INCET49848.2020.9154130.
  7. Ratnakar S, Rajeswari K, Jacob R. Prediction of heart disease using genetic algorithm for selection of optimal reduced set of attributes. Int J Adv Comput Eng Netw. 2013;1(2):51–5.
  8. Liang H, Tsui BY, Ni H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25(3):433–8.
  9. Al-Mallah MH, Aljizeeri A, Ahmed AM, et al. Prediction of diabetes mellitus type-II using machine learning techniques. Int J Med Inform. 2014;83(8):596–604.
  10. Dey S, Raza SA, Saha S. Machine learning techniques for diagnosis of diabetes and cardiovascular diseases. Int J Comput Appl. 2016;148(5):7–13.

Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 17/07/2024
Accepted 15/10/2024
Published 14/11/2024