We utilize data from healthcare fraternities and professionals comprising of disease cases and controls along with the demographic and clinical characteristics such as age, gender, BMI, comorbidities, history, symptoms, ESR value, drugs and others.
The data is filtered according to the requirements incorporating the characteristics that act as attributes/features for further processing. The data is pre-processed further and classification is carried out. Software for statistical computing is used for regression analysis via supervised ML techniques for probabilistic prediction of outcome in testing set imitating real word instances from target attribute in training set.
The initial data consists of disease cases vs controls along with demographic and clinical characteristics. The predictive model results in risk factors (predictors) along with statistical parameters. The statistical parameters indicate the observations that are predicted correctly, positive and negative observations that are accurately and appropriately predicted and probability of model ranking a random positive observation higher than a negative observation.
The predictive models are easy to interpret and effective and help in identifying high-risk individuals and for early diagnosis and management of cases before reaching later stages decreasing morbidity. The models are useful in preoperative risk stratification providing individualized treatment and tests for each patient and timely patient counselling and risk of hospitalization with higher precision. The models help in providing guidance and scientific basis for prevention and severity of diseases supporting clinical-decision making. The models guide in post-treatment benefits and risks and post-disease complications. The models aid in prediction of future onset that are vital for development of diseases and clinically valuable reducing unnecessary resources, minimizing harm with higher accuracy, reliability, predictive ability, practicability and utility. The models are potentially ideal for predicting the prognosis, incidence and remission of diseases with minimized prediction error. The risk factors and predictors of the models when amalgamated with existing diagnostic pattern provide concrete disease diagnosis that aid in moving a step further in the field of precision medicine for diseases improvising the available symptomatic treatment regime.