International Journal For Multidisciplinary Research

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Predicting Diseases With Machine Learning

Author(s) Ms. Bhoomika j Shetty, Ms. Ranjitha R
Country India
Abstract Abstract:
Disease prediction is the estimation of the likelihood that an individual will develop a specific disease at some future time‚ based on a constellation of symptoms‚ laboratory tests‚ medical history‚ lifestyle factors and clinical markers․ Early detection of disease may lead to optimal patient care‚ diagnosis‚ treatment‚ and ultimately prevention․ The exponential growth of electronic health records‚ wearable healthcare devices‚ and large medical databases has made machine learning a promising means to support disease prediction and assist healthcare professionals in making correct clinical decisions․
Traditionally‚ a doctor diagnoses a disease by experience‚ carrying out clinical examinations and laboratory tests․ This‚ while accepted and effective in many cases‚ can be a very time consuming‚ costly and highly skilled process․ Therefore‚ the similarity in the symptoms of diseases can hamper patient health due to delayed clinical decision making to initiate the treatment‚ and also raise the cost of healthcare․ As such‚ smart prediction systems for handling medical data are essential in providing reliable timely assistance through appropriate decisions and information to medical practitioners․
Machine learning refers to a data-driven approach that allows computers to discover hidden relationships and patterns in existing medical data, without being explicitly programmed for each individual condition. Machine learning algorithms can explore different healthcare information, such as patient demographics, symptoms, clinical measurements, diagnostic reports, and prior medical records to reveal complex trends that may be challenging to discern using traditional methods. The accessibility of large and various healthcare datasets has improved the capability of these models to generate accurate and reliable disease predictions.
Supervised machine learning algorithms have been widely used in disease prediction tasks because of its ability to classify and analyze medical data. Decision Tree algorithms are useful for clinical interpretation because they provide easily interpret able prediction rules based on patient characteristics. Random forest combine several decision tree is to improve prediction accuracy that are standard, which makes the prediction more accurate, stable and less prone to over fitting. The Support Vector Machine is useful in large scale medical data sets, because it can find the optimal boundary between various categories of diseases. Moreover, algorithms such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, Gradient Boosting, and Artificial Neural Networks have shown great potential in healthcare prediction applications. The efficiency of each algorithm is dependent on the dataset, complexity of the disease patterns and prediction goals.
Keywords Keywords: Artificial Intelligence, Machine Learning ,Naive Bayes, Support Vector Machine , Logistic Regression, Random Forest ,Gradient Boosting.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 4, July-August 2026
Published On 2026-07-14
DOI https://doi.org/10.36948/ijfmr.2026.v08i04.83800
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