Impact of Machine Learning Algorithms on Predicting Early Diagnosis of Chronic Diseases: A Statistical Approach

Abstract

The increase in the prevalence of chronic diseases and the proactive need to assess and predict such diseases' risks are on the rise. The binary classification accommodates the research of this study to compare ten machine learning algorithms, which is apt to evolute high surveillance for early diagnosis in predicting chronic diseases. The statistical approach is adopted for hypertension (HT) and diabetes mellitus (DM), analyzing early diagnosis in embracing a higher prediction accuracy and an efficient approach.

Research objectives: (1) To investigate and scrutinize the prediction of early diagnosis by comparing the performance of ten machine learning algorithms using statistical approaches, and (2) To evaluate and compare the noteworthy of machine learning algorithms using statistical approaches in predicting surveillance for early diagnosis of chronic diseases (HT & DM).

Research findings: Machine learning are highly adoptable. Health sectors are the most important entity to consider in sanctioning a healthier lifestyle. Machine learning is involved past years for foreseeing, presaging, and plunging the advanced research in the healthcare sector. Healthcare sectors, as well as novel researchers, have an optimistic exigency to apprehend and realize regarding assimilating and putting into place the machine learning algorithms to predict a macroscopical proliferation of unpredictable diseases in the healthcare districts. Preemptive treatment is highly successful to get rid of recourse intense and sentimental diseases risks, longitudinally estimating machine learning algorithms and it has a meaningful ef¬¦ in advance detection and pact to healthcare tranquility.

This concentration investigates the surveillance of machine learning algorithms using a statistical approach and it accommodates a novel ilk of au¬¥ along artsy-fartsy evaluation (Artificial Intelligence). Chronic Hype r¬ī tmis roots (HT) and Di abotes Melitis (DM) diseases are predicated ten protruding, notable, and yonder machine learning algorithms incommensurable with the capacity of utmost thousand and hundred prostate and extinction patients in attribute to leading and buxom cases in the Bangladesh healthcare sector.