METHOD FOR PREDICTING MULTIPLE DISEASES IN PATIENTS

Abstract

ABSTRACT A method (100) for predicting multiple diseases in. Further, the method comprising collecting patient data including clinical parameters, demographic information, and medical history. Further, the method (100) comprising the steps of pre-processing the collected data to handle missing values and normalize the data for analysis. Further, the method (100) comprising the steps of selecting a machine learning algorithm from a group consisting of decision trees, support vector machines, and logistic regression. Further, the method (100) comprising the steps of training the selected machine learning algorithm on the pre-processed patient data to create a predictive model. Further, the method (100) comprising the steps of utilizing the trained predictive model to generate disease predictions for new patient data inputs, thereby providing healthcare professionals with insights for diagnosis and treatment decisions. <>

Specification

Description:
FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of healthcare data mining and machine learning, and in particular, relates to a method for predicting multiple diseases in patients using machine learning algorithms that analyze clinical and demographic data to enhance diagnostic accuracy and improve patient outcomes.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The increasing complexity of healthcare data has led to a growing interest in the application of machine learning techni , Claims:1. A method (100) for predicting multiple diseases in patients, the method comprising the steps of:
collecting patient data including clinical parameters, demographic information, and medical history;
pre-processing the collected data to handle missing values and normalize the data for analysis;
selecting a machine learning algorithm from a group consisting of decision trees, support vector machines, and logistic regression;
training the selected machine learning algorithm on the pre-processed patient data to create a predictive model; and
utilizing the trained predictive model to generate disease predictions for new patient data inputs, thereby providing healthcare professionals with insights for diagnosis and treatment decisions.

2. The method (100) as claimed in claim 1, wherein the machine learning algorithm selected is a decision tree algorithm, which utilizes a series of binary decisions based on the patient data to predict the likelihood of disease occurrence.

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